Bioinformatics_resource_project
Andrea Tonina & Gloria Lugoboni
2023-06
Everything can be found in :https://github.com/iamandreatonina/Bioinformatics_Romanel_project-
Bioinformatics resource project with data set of Thyroid carcinoma
- Load the RData file. The following three data-frames are available:
- raw_counts_df = contains the raw RNA-seq counts
- c_anno_df = contains sample name and condition (case and control)
- r_ anno_df = contains the ENSEMBL genes ids, the length of the genes and the genes symbols
load('./file/Thyroid_carcinoma.RData')
View(raw_counts_df)
View(c_anno_df)
View(r_anno_df)- Update raw_count_df and r_anno_df extracting only protein coding genes.
- Use biomaRt package to retrieve the needed information
- Next tasks should use the new data-frames you have created
library(biomaRt)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.4.1
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::select() masks biomaRt::select()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(tidyverse)To display the available data bases
listMarts()## biomart version
## 1 ENSEMBL_MART_ENSEMBL Ensembl Genes 109
## 2 ENSEMBL_MART_MOUSE Mouse strains 109
## 3 ENSEMBL_MART_SNP Ensembl Variation 109
## 4 ENSEMBL_MART_FUNCGEN Ensembl Regulation 109The function useMart enables us to select a biomart database to use and the associated datasets. We select those with the parameters biomart and dataset respectively. The object returned is a Mart Class and can be seen as a connection to the Biomart database selected.
ensembl <- useMart(biomart = 'ensembl', dataset = 'hsapiens_gene_ensembl')By using the library biomart we can retrieve a query from BioMart database using a set of filters and corresponding values, the attributes we want to retrieve need to be specified and here correspond to ensembl_gene_id, external_gene_name, gene_biotype. Specifically ensembl_gene_id need to be filtered based on the ensembl_gene_id of our data frame r_ anno_df while gene_biotype represents the information we need, so if the genes are coding or not.
filtering <- r_anno_df$ensembl_gene_id
query <- getBM(attributes = c('ensembl_gene_id', 'external_gene_name','gene_biotype'),filters = c('ensembl_gene_id'),values = list(filtering),mart = ensembl)Here we filtered the query with only the genes that are coding genes.
query_protein_coding <- query %>%
filter(gene_biotype == 'protein_coding')After that, we filter both r_anno_df and raw_counts_df based on the enesembl_gene_id of the query containing the protein-coding gene.
r_anno_df_pro_cod <- r_anno_df %>%
filter(ensembl_gene_id %in% query_protein_coding$ensembl_gene_id)
raw_count_df_pro_cod <- raw_counts_df[which(rownames(raw_counts_df) %in% query_protein_coding$ensembl_gene_id),]- Perform differential expression analysis using edgeR package and select up- and down-regulated genes using a p-value cutoff of 0.01, a log fold change ratio >1.5 for up-regulated genes and < (-1.5) for down-regulated genes and a log CPM >1. Relax the thresholds if no or few results are available.
- Use the workflow we developed during the course
- Filter raw counts data retaining only genes with a raw count >20 in at least 5 Cases or 5 Control samples
- Create a volcano plot of your results
- Create an annotated heatmap focusing only on up- and downregulated genes
library(edgeR)## Loading required package: limmalibrary(fgsea)Here we set the thresholds to remove genes with low signals, this is needed because those genes have low statistical power and give us no information.
count_thr <- 20 # raw counts data retaining only genes with a raw count >20
repl_thr <- 5 # 5 Cases or 5 Control samplesWe count how many replicates in each group satisfy the count threshold. This passage is needed to apply the first threshold and eliminate the rows which don’t satisfy it. We create the filter_vec, a vector of samples that surpass the thresholds with raw count >= 20.
filter_vec <- apply(raw_count_df_pro_cod,1, function(y) max(by(y,c_anno_df$condition, function(x) sum(x >= count_thr))))
#summary of the vector
table(filter_vec)## filter_vec
## 0 1 2 3 4 5 6 7 8 9 10 11 12
## 4665 339 172 129 106 80 66 75 50 57 50 54 40
## 13 14 15 16 17 18 19 20 21 22 23 24 25
## 49 43 38 42 39 38 37 36 32 39 34 30 33
## 26 27 28 29 30 31 32 33 34 35 36 37 38
## 42 41 50 33 34 38 41 37 35 35 43 46 34
## 39 40 41 42 43 44 45 46 47 48 49 50
## 53 52 64 64 80 78 103 146 161 236 517 13723Then we create a new dataframe filtered based on the filter_vec with a threshold on the replicates
filter_count_df <- raw_count_df_pro_cod[filter_vec >= repl_thr,]
dim(filter_count_df) # the rows represent the number of transcripts that satisfy the threshold ## [1] 16748 100Update the gene annotation using the filter. The gene annotation file must be consistent with the data files we are using.
filter_anno_df <- r_anno_df_pro_cod[rownames(filter_count_df),] # we filter the gene annotation file
dim(filter_anno_df) # the number of row is equal to the number of row of the file filter_count_df## [1] 16748 3Now we can proceed with the DEG analysis using edgeR. First, we create a DGElist object using the function DGEList. This object contains all the information needed for the next steps (counts (=transcript counts), groups (=), and samples).
edge_c <- DGEList(counts = filter_count_df, group = c_anno_df$condition, samples = c_anno_df, genes = filter_anno_df)
edge_c## An object of class "DGEList"
## $counts
## TCGA-DJ-A2Q1-01A TCGA-H2-A2K9-11A TCGA-H2-A3RI-11A
## ENSG00000187634 1385 596 728
## ENSG00000188976 4076 4114 3988
## ENSG00000187961 285 226 243
## ENSG00000187583 650 54 37
## ENSG00000187642 570 22 12
## TCGA-DJ-A4V4-01A TCGA-EL-A3T9-01A TCGA-BJ-A0ZB-01A
## ENSG00000187634 1336 1173 1071
## ENSG00000188976 4114 5712 5383
## ENSG00000187961 338 623 240
## ENSG00000187583 557 931 489
## ENSG00000187642 298 324 181
## TCGA-EL-A3H1-01A TCGA-H2-A3RI-01A TCGA-EL-A3GR-01A
## ENSG00000187634 856 2180 825
## ENSG00000188976 4703 6086 3074
## ENSG00000187961 248 501 294
## ENSG00000187583 53 746 567
## ENSG00000187642 12 236 143
## TCGA-DJ-A3UN-01A TCGA-DO-A1JZ-01A TCGA-EL-A3GU-01A
## ENSG00000187634 1358 2147 729
## ENSG00000188976 5933 5130 3542
## ENSG00000187961 408 314 282
## ENSG00000187583 767 331 536
## ENSG00000187642 325 107 163
## TCGA-DJ-A1QM-01A TCGA-EM-A1CT-11A TCGA-EL-A3CZ-01A
## ENSG00000187634 1427 823 330
## ENSG00000188976 5293 4467 2769
## ENSG00000187961 288 145 133
## ENSG00000187583 428 24 105
## ENSG00000187642 146 17 30
## TCGA-EL-A3ZS-11A TCGA-J8-A3YE-01A TCGA-ET-A2N4-01A
## ENSG00000187634 712 833 2508
## ENSG00000188976 1946 4416 6531
## ENSG00000187961 195 613 394
## ENSG00000187583 40 526 967
## ENSG00000187642 35 156 416
## TCGA-EM-A4FO-01A TCGA-EL-A3ZO-11A TCGA-ET-A4KN-01A
## ENSG00000187634 1389 847 4158
## ENSG00000188976 4618 3365 4921
## ENSG00000187961 519 202 132
## ENSG00000187583 545 49 396
## ENSG00000187642 277 26 569
## TCGA-EL-A3T1-11A TCGA-ET-A2N5-11B TCGA-BJ-A2N8-11A
## ENSG00000187634 756 777 947
## ENSG00000188976 3407 5134 6688
## ENSG00000187961 169 197 190
## ENSG00000187583 69 7 87
## ENSG00000187642 33 9 29
## TCGA-FY-A3I4-01A TCGA-EL-A3ZQ-11A TCGA-DJ-A2Q2-01A
## ENSG00000187634 2044 902 885
## ENSG00000188976 6328 4849 5436
## ENSG00000187961 372 413 242
## ENSG00000187583 1059 55 153
## ENSG00000187642 355 25 51
## TCGA-EL-A3ZP-11A TCGA-EL-A3GQ-01A TCGA-EL-A3TA-11A
## ENSG00000187634 536 2009 838
## ENSG00000188976 3141 4217 5328
## ENSG00000187961 241 309 226
## ENSG00000187583 59 373 23
## ENSG00000187642 20 129 16
## TCGA-CE-A481-01A TCGA-E8-A438-01A TCGA-EL-A3H7-11A
## ENSG00000187634 828 3638 779
## ENSG00000188976 4236 5774 4484
## ENSG00000187961 430 733 188
## ENSG00000187583 323 1190 73
## ENSG00000187642 133 673 31
## TCGA-EL-A3GZ-11A TCGA-E8-A413-01A TCGA-BJ-A28X-11A
## ENSG00000187634 518 1230 834
## ENSG00000188976 3293 4564 4379
## ENSG00000187961 100 431 527
## ENSG00000187583 12 611 116
## ENSG00000187642 6 188 27
## TCGA-EM-A2CS-01A TCGA-ET-A3BX-01A TCGA-DJ-A13V-01A
## ENSG00000187634 1512 2770 1882
## ENSG00000188976 5074 5293 4500
## ENSG00000187961 249 310 229
## ENSG00000187583 279 1276 688
## ENSG00000187642 92 457 250
## TCGA-KS-A41I-11A TCGA-E8-A44K-01A TCGA-ET-A3DW-11A
## ENSG00000187634 639 601 672
## ENSG00000188976 3641 3296 3835
## ENSG00000187961 353 359 125
## ENSG00000187583 110 442 52
## ENSG00000187642 44 155 19
## TCGA-EL-A3ZL-11A TCGA-EL-A4JV-01A TCGA-FE-A231-01A
## ENSG00000187634 665 711 586
## ENSG00000188976 4600 3679 5244
## ENSG00000187961 207 252 284
## ENSG00000187583 47 136 713
## ENSG00000187642 29 41 209
## TCGA-BJ-A28R-11A TCGA-EM-A1CS-11A TCGA-FE-A3PA-01A
## ENSG00000187634 623 878 1346
## ENSG00000188976 3382 4261 4057
## ENSG00000187961 169 148 763
## ENSG00000187583 48 22 716
## ENSG00000187642 13 14 633
## TCGA-EM-A2CQ-01A TCGA-EL-A3TB-11A TCGA-BJ-A28W-11A
## ENSG00000187634 741 987 624
## ENSG00000188976 5195 4640 2636
## ENSG00000187961 158 127 128
## ENSG00000187583 177 9 129
## ENSG00000187642 49 7 76
## TCGA-KS-A41J-11A TCGA-EL-A3T2-11A TCGA-KS-A41L-11A
## ENSG00000187634 590 572 736
## ENSG00000188976 4202 4169 4679
## ENSG00000187961 306 206 375
## ENSG00000187583 53 18 28
## ENSG00000187642 13 22 17
## TCGA-EL-A3T0-11A TCGA-DJ-A3UP-01A TCGA-DJ-A4V0-01A
## ENSG00000187634 616 772 962
## ENSG00000188976 3781 4261 3834
## ENSG00000187961 193 315 404
## ENSG00000187583 23 350 258
## ENSG00000187642 12 122 79
## TCGA-EL-A3ZK-11A TCGA-BJ-A3PT-01A TCGA-EM-A3OA-01A
## ENSG00000187634 980 2430 1895
## ENSG00000188976 3483 4515 7998
## ENSG00000187961 328 229 577
## ENSG00000187583 118 276 156
## ENSG00000187642 47 156 47
## TCGA-FY-A3TY-11A TCGA-EL-A3MY-11A TCGA-FY-A3R9-01A
## ENSG00000187634 507 1034 937
## ENSG00000188976 3192 3762 3901
## ENSG00000187961 244 189 182
## ENSG00000187583 15 45 78
## ENSG00000187642 11 14 30
## TCGA-BJ-A3PR-11A TCGA-EL-A3H2-11A TCGA-ET-A2MY-01A
## ENSG00000187634 2733 673 1765
## ENSG00000188976 5395 4086 5239
## ENSG00000187961 291 123 206
## ENSG00000187583 62 20 509
## ENSG00000187642 71 4 209
## TCGA-ET-A3DP-11A TCGA-L6-A4ET-01A TCGA-BJ-A3PU-11A
## ENSG00000187634 643 3035 1017
## ENSG00000188976 4576 6615 5129
## ENSG00000187961 58 651 240
## ENSG00000187583 13 1070 38
## ENSG00000187642 5 366 18
## TCGA-DO-A2HM-01B TCGA-E8-A2JQ-11A TCGA-EL-A3ZT-11A
## ENSG00000187634 592 644 424
## ENSG00000188976 3982 3262 3511
## ENSG00000187961 480 233 259
## ENSG00000187583 413 73 23
## ENSG00000187642 132 28 8
## TCGA-EL-A3MW-11A TCGA-BJ-A28T-01A TCGA-DO-A1JZ-11A
## ENSG00000187634 676 293 469
## ENSG00000188976 3402 1600 3863
## ENSG00000187961 109 87 95
## ENSG00000187583 21 71 15
## ENSG00000187642 4 25 3
## TCGA-EL-A3N3-11A TCGA-BJ-A290-11A TCGA-E8-A242-01A
## ENSG00000187634 723 812 2043
## ENSG00000188976 4296 4258 5579
## ENSG00000187961 201 149 303
## ENSG00000187583 40 25 1434
## ENSG00000187642 18 6 497
## TCGA-ET-A39L-01A TCGA-EL-A3MY-01A TCGA-EL-A3T8-11A
## ENSG00000187634 1026 1248 856
## ENSG00000188976 5111 4227 3813
## ENSG00000187961 188 336 283
## ENSG00000187583 264 991 69
## ENSG00000187642 104 337 39
## TCGA-EM-A1CV-11A TCGA-EM-A1CW-11A TCGA-EL-A3N2-11A
## ENSG00000187634 702 169 517
## ENSG00000188976 4533 1821 3685
## ENSG00000187961 148 43 155
## ENSG00000187583 28 7 40
## ENSG00000187642 11 0 18
## TCGA-EL-A3T3-11A TCGA-BJ-A45G-01A TCGA-EL-A3CY-01A
## ENSG00000187634 1016 842 987
## ENSG00000188976 4499 4031 4132
## ENSG00000187961 257 256 383
## ENSG00000187583 22 311 301
## ENSG00000187642 14 121 111
## TCGA-EL-A3ZR-11A TCGA-EL-A3T7-11A TCGA-CE-A3ME-01A
## ENSG00000187634 625 600 1428
## ENSG00000188976 3827 3951 5277
## ENSG00000187961 304 272 320
## ENSG00000187583 109 32 378
## ENSG00000187642 250 26 154
## TCGA-EL-A3T6-11A TCGA-KS-A4ID-01A TCGA-ET-A3BP-01A
## ENSG00000187634 1130 867 1631
## ENSG00000188976 4429 3260 4683
## ENSG00000187961 307 241 275
## ENSG00000187583 96 444 466
## ENSG00000187642 27 197 232
## TCGA-EL-A3ZG-11A TCGA-ET-A2MX-11C TCGA-BJ-A18Z-01A
## ENSG00000187634 668 765 718
## ENSG00000188976 2450 3985 2503
## ENSG00000187961 122 204 286
## ENSG00000187583 8 32 549
## ENSG00000187642 8 13 245
## TCGA-GE-A2C6-11A TCGA-BJ-A2N9-11A TCGA-ET-A39N-01A
## ENSG00000187634 368 867 1338
## ENSG00000188976 2513 4565 5517
## ENSG00000187961 125 248 163
## ENSG00000187583 34 75 94
## ENSG00000187642 22 26 55
## TCGA-DJ-A2QC-01A
## ENSG00000187634 3174
## ENSG00000188976 6177
## ENSG00000187961 312
## ENSG00000187583 610
## ENSG00000187642 346
## 16743 more rows ...
##
## $samples
## group lib.size norm.factors sample condition
## TCGA-DJ-A2Q1-01A case 68063260 1 TCGA-DJ-A2Q1-01A case
## TCGA-H2-A2K9-11A control 79992173 1 TCGA-H2-A2K9-11A control
## TCGA-H2-A3RI-11A control 76205032 1 TCGA-H2-A3RI-11A control
## TCGA-DJ-A4V4-01A case 57746743 1 TCGA-DJ-A4V4-01A case
## TCGA-EL-A3T9-01A case 69959466 1 TCGA-EL-A3T9-01A case
## 95 more rows ...
##
## $genes
## ensembl_gene_id external_gene_name length
## ENSG00000187634 ENSG00000187634 SAMD11 20652
## ENSG00000188976 ENSG00000188976 NOC2L 15106
## ENSG00000187961 ENSG00000187961 KLHL17 5135
## ENSG00000187583 ENSG00000187583 PLEKHN1 9383
## ENSG00000187642 ENSG00000187642 PERM1 6919
## 16743 more rows ...Normalization using calcNormFactor and the TMM method. This method allows us to perform intra and inter-normalization of the data. A scaling factor is calculated based on the library size, plus with the TMM method the normalization focuses on the convergence of the means.
edge_n <- calcNormFactors(edge_c,method = 'TMM')
edge_n # identical to edge_c just a column called normalization factor is added.## An object of class "DGEList"
## $counts
## TCGA-DJ-A2Q1-01A TCGA-H2-A2K9-11A TCGA-H2-A3RI-11A
## ENSG00000187634 1385 596 728
## ENSG00000188976 4076 4114 3988
## ENSG00000187961 285 226 243
## ENSG00000187583 650 54 37
## ENSG00000187642 570 22 12
## TCGA-DJ-A4V4-01A TCGA-EL-A3T9-01A TCGA-BJ-A0ZB-01A
## ENSG00000187634 1336 1173 1071
## ENSG00000188976 4114 5712 5383
## ENSG00000187961 338 623 240
## ENSG00000187583 557 931 489
## ENSG00000187642 298 324 181
## TCGA-EL-A3H1-01A TCGA-H2-A3RI-01A TCGA-EL-A3GR-01A
## ENSG00000187634 856 2180 825
## ENSG00000188976 4703 6086 3074
## ENSG00000187961 248 501 294
## ENSG00000187583 53 746 567
## ENSG00000187642 12 236 143
## TCGA-DJ-A3UN-01A TCGA-DO-A1JZ-01A TCGA-EL-A3GU-01A
## ENSG00000187634 1358 2147 729
## ENSG00000188976 5933 5130 3542
## ENSG00000187961 408 314 282
## ENSG00000187583 767 331 536
## ENSG00000187642 325 107 163
## TCGA-DJ-A1QM-01A TCGA-EM-A1CT-11A TCGA-EL-A3CZ-01A
## ENSG00000187634 1427 823 330
## ENSG00000188976 5293 4467 2769
## ENSG00000187961 288 145 133
## ENSG00000187583 428 24 105
## ENSG00000187642 146 17 30
## TCGA-EL-A3ZS-11A TCGA-J8-A3YE-01A TCGA-ET-A2N4-01A
## ENSG00000187634 712 833 2508
## ENSG00000188976 1946 4416 6531
## ENSG00000187961 195 613 394
## ENSG00000187583 40 526 967
## ENSG00000187642 35 156 416
## TCGA-EM-A4FO-01A TCGA-EL-A3ZO-11A TCGA-ET-A4KN-01A
## ENSG00000187634 1389 847 4158
## ENSG00000188976 4618 3365 4921
## ENSG00000187961 519 202 132
## ENSG00000187583 545 49 396
## ENSG00000187642 277 26 569
## TCGA-EL-A3T1-11A TCGA-ET-A2N5-11B TCGA-BJ-A2N8-11A
## ENSG00000187634 756 777 947
## ENSG00000188976 3407 5134 6688
## ENSG00000187961 169 197 190
## ENSG00000187583 69 7 87
## ENSG00000187642 33 9 29
## TCGA-FY-A3I4-01A TCGA-EL-A3ZQ-11A TCGA-DJ-A2Q2-01A
## ENSG00000187634 2044 902 885
## ENSG00000188976 6328 4849 5436
## ENSG00000187961 372 413 242
## ENSG00000187583 1059 55 153
## ENSG00000187642 355 25 51
## TCGA-EL-A3ZP-11A TCGA-EL-A3GQ-01A TCGA-EL-A3TA-11A
## ENSG00000187634 536 2009 838
## ENSG00000188976 3141 4217 5328
## ENSG00000187961 241 309 226
## ENSG00000187583 59 373 23
## ENSG00000187642 20 129 16
## TCGA-CE-A481-01A TCGA-E8-A438-01A TCGA-EL-A3H7-11A
## ENSG00000187634 828 3638 779
## ENSG00000188976 4236 5774 4484
## ENSG00000187961 430 733 188
## ENSG00000187583 323 1190 73
## ENSG00000187642 133 673 31
## TCGA-EL-A3GZ-11A TCGA-E8-A413-01A TCGA-BJ-A28X-11A
## ENSG00000187634 518 1230 834
## ENSG00000188976 3293 4564 4379
## ENSG00000187961 100 431 527
## ENSG00000187583 12 611 116
## ENSG00000187642 6 188 27
## TCGA-EM-A2CS-01A TCGA-ET-A3BX-01A TCGA-DJ-A13V-01A
## ENSG00000187634 1512 2770 1882
## ENSG00000188976 5074 5293 4500
## ENSG00000187961 249 310 229
## ENSG00000187583 279 1276 688
## ENSG00000187642 92 457 250
## TCGA-KS-A41I-11A TCGA-E8-A44K-01A TCGA-ET-A3DW-11A
## ENSG00000187634 639 601 672
## ENSG00000188976 3641 3296 3835
## ENSG00000187961 353 359 125
## ENSG00000187583 110 442 52
## ENSG00000187642 44 155 19
## TCGA-EL-A3ZL-11A TCGA-EL-A4JV-01A TCGA-FE-A231-01A
## ENSG00000187634 665 711 586
## ENSG00000188976 4600 3679 5244
## ENSG00000187961 207 252 284
## ENSG00000187583 47 136 713
## ENSG00000187642 29 41 209
## TCGA-BJ-A28R-11A TCGA-EM-A1CS-11A TCGA-FE-A3PA-01A
## ENSG00000187634 623 878 1346
## ENSG00000188976 3382 4261 4057
## ENSG00000187961 169 148 763
## ENSG00000187583 48 22 716
## ENSG00000187642 13 14 633
## TCGA-EM-A2CQ-01A TCGA-EL-A3TB-11A TCGA-BJ-A28W-11A
## ENSG00000187634 741 987 624
## ENSG00000188976 5195 4640 2636
## ENSG00000187961 158 127 128
## ENSG00000187583 177 9 129
## ENSG00000187642 49 7 76
## TCGA-KS-A41J-11A TCGA-EL-A3T2-11A TCGA-KS-A41L-11A
## ENSG00000187634 590 572 736
## ENSG00000188976 4202 4169 4679
## ENSG00000187961 306 206 375
## ENSG00000187583 53 18 28
## ENSG00000187642 13 22 17
## TCGA-EL-A3T0-11A TCGA-DJ-A3UP-01A TCGA-DJ-A4V0-01A
## ENSG00000187634 616 772 962
## ENSG00000188976 3781 4261 3834
## ENSG00000187961 193 315 404
## ENSG00000187583 23 350 258
## ENSG00000187642 12 122 79
## TCGA-EL-A3ZK-11A TCGA-BJ-A3PT-01A TCGA-EM-A3OA-01A
## ENSG00000187634 980 2430 1895
## ENSG00000188976 3483 4515 7998
## ENSG00000187961 328 229 577
## ENSG00000187583 118 276 156
## ENSG00000187642 47 156 47
## TCGA-FY-A3TY-11A TCGA-EL-A3MY-11A TCGA-FY-A3R9-01A
## ENSG00000187634 507 1034 937
## ENSG00000188976 3192 3762 3901
## ENSG00000187961 244 189 182
## ENSG00000187583 15 45 78
## ENSG00000187642 11 14 30
## TCGA-BJ-A3PR-11A TCGA-EL-A3H2-11A TCGA-ET-A2MY-01A
## ENSG00000187634 2733 673 1765
## ENSG00000188976 5395 4086 5239
## ENSG00000187961 291 123 206
## ENSG00000187583 62 20 509
## ENSG00000187642 71 4 209
## TCGA-ET-A3DP-11A TCGA-L6-A4ET-01A TCGA-BJ-A3PU-11A
## ENSG00000187634 643 3035 1017
## ENSG00000188976 4576 6615 5129
## ENSG00000187961 58 651 240
## ENSG00000187583 13 1070 38
## ENSG00000187642 5 366 18
## TCGA-DO-A2HM-01B TCGA-E8-A2JQ-11A TCGA-EL-A3ZT-11A
## ENSG00000187634 592 644 424
## ENSG00000188976 3982 3262 3511
## ENSG00000187961 480 233 259
## ENSG00000187583 413 73 23
## ENSG00000187642 132 28 8
## TCGA-EL-A3MW-11A TCGA-BJ-A28T-01A TCGA-DO-A1JZ-11A
## ENSG00000187634 676 293 469
## ENSG00000188976 3402 1600 3863
## ENSG00000187961 109 87 95
## ENSG00000187583 21 71 15
## ENSG00000187642 4 25 3
## TCGA-EL-A3N3-11A TCGA-BJ-A290-11A TCGA-E8-A242-01A
## ENSG00000187634 723 812 2043
## ENSG00000188976 4296 4258 5579
## ENSG00000187961 201 149 303
## ENSG00000187583 40 25 1434
## ENSG00000187642 18 6 497
## TCGA-ET-A39L-01A TCGA-EL-A3MY-01A TCGA-EL-A3T8-11A
## ENSG00000187634 1026 1248 856
## ENSG00000188976 5111 4227 3813
## ENSG00000187961 188 336 283
## ENSG00000187583 264 991 69
## ENSG00000187642 104 337 39
## TCGA-EM-A1CV-11A TCGA-EM-A1CW-11A TCGA-EL-A3N2-11A
## ENSG00000187634 702 169 517
## ENSG00000188976 4533 1821 3685
## ENSG00000187961 148 43 155
## ENSG00000187583 28 7 40
## ENSG00000187642 11 0 18
## TCGA-EL-A3T3-11A TCGA-BJ-A45G-01A TCGA-EL-A3CY-01A
## ENSG00000187634 1016 842 987
## ENSG00000188976 4499 4031 4132
## ENSG00000187961 257 256 383
## ENSG00000187583 22 311 301
## ENSG00000187642 14 121 111
## TCGA-EL-A3ZR-11A TCGA-EL-A3T7-11A TCGA-CE-A3ME-01A
## ENSG00000187634 625 600 1428
## ENSG00000188976 3827 3951 5277
## ENSG00000187961 304 272 320
## ENSG00000187583 109 32 378
## ENSG00000187642 250 26 154
## TCGA-EL-A3T6-11A TCGA-KS-A4ID-01A TCGA-ET-A3BP-01A
## ENSG00000187634 1130 867 1631
## ENSG00000188976 4429 3260 4683
## ENSG00000187961 307 241 275
## ENSG00000187583 96 444 466
## ENSG00000187642 27 197 232
## TCGA-EL-A3ZG-11A TCGA-ET-A2MX-11C TCGA-BJ-A18Z-01A
## ENSG00000187634 668 765 718
## ENSG00000188976 2450 3985 2503
## ENSG00000187961 122 204 286
## ENSG00000187583 8 32 549
## ENSG00000187642 8 13 245
## TCGA-GE-A2C6-11A TCGA-BJ-A2N9-11A TCGA-ET-A39N-01A
## ENSG00000187634 368 867 1338
## ENSG00000188976 2513 4565 5517
## ENSG00000187961 125 248 163
## ENSG00000187583 34 75 94
## ENSG00000187642 22 26 55
## TCGA-DJ-A2QC-01A
## ENSG00000187634 3174
## ENSG00000188976 6177
## ENSG00000187961 312
## ENSG00000187583 610
## ENSG00000187642 346
## 16743 more rows ...
##
## $samples
## group lib.size norm.factors sample condition
## TCGA-DJ-A2Q1-01A case 68063260 0.7881489 TCGA-DJ-A2Q1-01A case
## TCGA-H2-A2K9-11A control 79992173 1.0866990 TCGA-H2-A2K9-11A control
## TCGA-H2-A3RI-11A control 76205032 1.0931174 TCGA-H2-A3RI-11A control
## TCGA-DJ-A4V4-01A case 57746743 0.9422199 TCGA-DJ-A4V4-01A case
## TCGA-EL-A3T9-01A case 69959466 0.9466800 TCGA-EL-A3T9-01A case
## 95 more rows ...
##
## $genes
## ensembl_gene_id external_gene_name length
## ENSG00000187634 ENSG00000187634 SAMD11 20652
## ENSG00000188976 ENSG00000188976 NOC2L 15106
## ENSG00000187961 ENSG00000187961 KLHL17 5135
## ENSG00000187583 ENSG00000187583 PLEKHN1 9383
## ENSG00000187642 ENSG00000187642 PERM1 6919
## 16743 more rows ...We can then create a CPM table to normalize expression values. We use the CPM function and transform the result in a dataframe with function as.data.frame(). Also we create another CPM table in log10 transformation.
cpm_table <- as.data.frame(round(cpm(edge_n),2)) # the library size is scaled by the normalization factor
head(cpm_table)## TCGA-DJ-A2Q1-01A TCGA-H2-A2K9-11A TCGA-H2-A3RI-11A
## ENSG00000187634 25.82 6.86 8.74
## ENSG00000188976 75.98 47.33 47.87
## ENSG00000187961 5.31 2.60 2.92
## ENSG00000187583 12.12 0.62 0.44
## ENSG00000187642 10.63 0.25 0.14
## ENSG00000188290 2.16 0.63 0.95
## TCGA-DJ-A4V4-01A TCGA-EL-A3T9-01A TCGA-BJ-A0ZB-01A
## ENSG00000187634 24.55 17.71 14.23
## ENSG00000188976 75.61 86.25 71.53
## ENSG00000187961 6.21 9.41 3.19
## ENSG00000187583 10.24 14.06 6.50
## ENSG00000187642 5.48 4.89 2.41
## ENSG00000188290 7.41 3.62 3.15
## TCGA-EL-A3H1-01A TCGA-H2-A3RI-01A TCGA-EL-A3GR-01A
## ENSG00000187634 11.13 25.51 14.57
## ENSG00000188976 61.14 71.23 54.27
## ENSG00000187961 3.22 5.86 5.19
## ENSG00000187583 0.69 8.73 10.01
## ENSG00000187642 0.16 2.76 2.52
## ENSG00000188290 3.55 2.97 7.38
## TCGA-DJ-A3UN-01A TCGA-DO-A1JZ-01A TCGA-EL-A3GU-01A
## ENSG00000187634 19.89 34.35 12.24
## ENSG00000188976 86.91 82.07 59.46
## ENSG00000187961 5.98 5.02 4.73
## ENSG00000187583 11.24 5.30 9.00
## ENSG00000187642 4.76 1.71 2.74
## ENSG00000188290 3.68 3.26 13.11
## TCGA-DJ-A1QM-01A TCGA-EM-A1CT-11A TCGA-EL-A3CZ-01A
## ENSG00000187634 16.45 10.71 5.87
## ENSG00000188976 61.02 58.15 49.23
## ENSG00000187961 3.32 1.89 2.36
## ENSG00000187583 4.93 0.31 1.87
## ENSG00000187642 1.68 0.22 0.53
## ENSG00000188290 1.78 1.45 1.01
## TCGA-EL-A3ZS-11A TCGA-J8-A3YE-01A TCGA-ET-A2N4-01A
## ENSG00000187634 29.30 11.81 39.34
## ENSG00000188976 80.09 62.63 102.45
## ENSG00000187961 8.03 8.69 6.18
## ENSG00000187583 1.65 7.46 15.17
## ENSG00000187642 1.44 2.21 6.53
## ENSG00000188290 16.91 9.73 7.81
## TCGA-EM-A4FO-01A TCGA-EL-A3ZO-11A TCGA-ET-A4KN-01A
## ENSG00000187634 20.25 12.50 201.92
## ENSG00000188976 67.33 49.68 238.97
## ENSG00000187961 7.57 2.98 6.41
## ENSG00000187583 7.95 0.72 19.23
## ENSG00000187642 4.04 0.38 27.63
## ENSG00000188290 8.75 10.69 18.79
## TCGA-EL-A3T1-11A TCGA-ET-A2N5-11B TCGA-BJ-A2N8-11A
## ENSG00000187634 11.99 9.82 8.84
## ENSG00000188976 54.06 64.87 62.45
## ENSG00000187961 2.68 2.49 1.77
## ENSG00000187583 1.09 0.09 0.81
## ENSG00000187642 0.52 0.11 0.27
## ENSG00000188290 3.13 0.86 0.57
## TCGA-FY-A3I4-01A TCGA-EL-A3ZQ-11A TCGA-DJ-A2Q2-01A
## ENSG00000187634 21.71 10.34 11.54
## ENSG00000188976 67.20 55.58 70.86
## ENSG00000187961 3.95 4.73 3.15
## ENSG00000187583 11.25 0.63 1.99
## ENSG00000187642 3.77 0.29 0.66
## ENSG00000188290 8.85 1.93 1.94
## TCGA-EL-A3ZP-11A TCGA-EL-A3GQ-01A TCGA-EL-A3TA-11A
## ENSG00000187634 8.31 29.53 9.99
## ENSG00000188976 48.67 61.98 63.50
## ENSG00000187961 3.73 4.54 2.69
## ENSG00000187583 0.91 5.48 0.27
## ENSG00000187642 0.31 1.90 0.19
## ENSG00000188290 2.28 1.44 0.68
## TCGA-CE-A481-01A TCGA-E8-A438-01A TCGA-EL-A3H7-11A
## ENSG00000187634 11.63 62.13 11.05
## ENSG00000188976 59.52 98.61 63.62
## ENSG00000187961 6.04 12.52 2.67
## ENSG00000187583 4.54 20.32 1.04
## ENSG00000187642 1.87 11.49 0.44
## ENSG00000188290 4.75 24.20 2.82
## TCGA-EL-A3GZ-11A TCGA-E8-A413-01A TCGA-BJ-A28X-11A
## ENSG00000187634 8.49 17.65 10.40
## ENSG00000188976 53.95 65.48 54.59
## ENSG00000187961 1.64 6.18 6.57
## ENSG00000187583 0.20 8.77 1.45
## ENSG00000187642 0.10 2.70 0.34
## ENSG00000188290 1.05 16.08 0.66
## TCGA-EM-A2CS-01A TCGA-ET-A3BX-01A TCGA-DJ-A13V-01A
## ENSG00000187634 21.93 42.67 35.63
## ENSG00000188976 73.61 81.54 85.20
## ENSG00000187961 3.61 4.78 4.34
## ENSG00000187583 4.05 19.66 13.03
## ENSG00000187642 1.33 7.04 4.73
## ENSG00000188290 3.21 6.67 5.28
## TCGA-KS-A41I-11A TCGA-E8-A44K-01A TCGA-ET-A3DW-11A
## ENSG00000187634 10.20 11.74 9.91
## ENSG00000188976 58.11 64.39 56.55
## ENSG00000187961 5.63 7.01 1.84
## ENSG00000187583 1.76 8.63 0.77
## ENSG00000187642 0.70 3.03 0.28
## ENSG00000188290 4.39 6.64 1.87
## TCGA-EL-A3ZL-11A TCGA-EL-A4JV-01A TCGA-FE-A231-01A
## ENSG00000187634 8.49 11.85 7.20
## ENSG00000188976 58.72 61.32 64.47
## ENSG00000187961 2.64 4.20 3.49
## ENSG00000187583 0.60 2.27 8.77
## ENSG00000187642 0.37 0.68 2.57
## ENSG00000188290 0.71 6.22 1.65
## TCGA-BJ-A28R-11A TCGA-EM-A1CS-11A TCGA-FE-A3PA-01A
## ENSG00000187634 9.33 12.88 19.75
## ENSG00000188976 50.65 62.52 59.54
## ENSG00000187961 2.53 2.17 11.20
## ENSG00000187583 0.72 0.32 10.51
## ENSG00000187642 0.19 0.21 9.29
## ENSG00000188290 1.57 1.28 22.07
## TCGA-EM-A2CQ-01A TCGA-EL-A3TB-11A TCGA-BJ-A28W-11A
## ENSG00000187634 9.29 12.26 19.19
## ENSG00000188976 65.15 57.64 81.07
## ENSG00000187961 1.98 1.58 3.94
## ENSG00000187583 2.22 0.11 3.97
## ENSG00000187642 0.61 0.09 2.34
## ENSG00000188290 3.21 0.58 6.89
## TCGA-KS-A41J-11A TCGA-EL-A3T2-11A TCGA-KS-A41L-11A
## ENSG00000187634 6.89 7.78 8.96
## ENSG00000188976 49.05 56.74 56.93
## ENSG00000187961 3.57 2.80 4.56
## ENSG00000187583 0.62 0.24 0.34
## ENSG00000187642 0.15 0.30 0.21
## ENSG00000188290 2.70 0.82 1.97
## TCGA-EL-A3T0-11A TCGA-DJ-A3UP-01A TCGA-DJ-A4V0-01A
## ENSG00000187634 9.04 10.24 13.97
## ENSG00000188976 55.46 56.52 55.69
## ENSG00000187961 2.83 4.18 5.87
## ENSG00000187583 0.34 4.64 3.75
## ENSG00000187642 0.18 1.62 1.15
## ENSG00000188290 0.97 1.01 2.40
## TCGA-EL-A3ZK-11A TCGA-BJ-A3PT-01A TCGA-EM-A3OA-01A
## ENSG00000187634 15.34 44.72 15.49
## ENSG00000188976 54.52 83.09 65.36
## ENSG00000187961 5.13 4.21 4.72
## ENSG00000187583 1.85 5.08 1.27
## ENSG00000187642 0.74 2.87 0.38
## ENSG00000188290 1.28 3.68 5.58
## TCGA-FY-A3TY-11A TCGA-EL-A3MY-11A TCGA-FY-A3R9-01A
## ENSG00000187634 8.31 13.88 14.08
## ENSG00000188976 52.32 50.49 58.63
## ENSG00000187961 4.00 2.54 2.74
## ENSG00000187583 0.25 0.60 1.17
## ENSG00000187642 0.18 0.19 0.45
## ENSG00000188290 1.16 2.81 1.46
## TCGA-BJ-A3PR-11A TCGA-EL-A3H2-11A TCGA-ET-A2MY-01A
## ENSG00000187634 51.92 8.87 30.73
## ENSG00000188976 102.50 53.86 91.21
## ENSG00000187961 5.53 1.62 3.59
## ENSG00000187583 1.18 0.26 8.86
## ENSG00000187642 1.35 0.05 3.64
## ENSG00000188290 4.52 0.45 3.19
## TCGA-ET-A3DP-11A TCGA-L6-A4ET-01A TCGA-BJ-A3PU-11A
## ENSG00000187634 8.28 49.16 11.42
## ENSG00000188976 58.93 107.15 57.60
## ENSG00000187961 0.75 10.54 2.70
## ENSG00000187583 0.17 17.33 0.43
## ENSG00000187642 0.06 5.93 0.20
## ENSG00000188290 1.20 9.61 1.12
## TCGA-DO-A2HM-01B TCGA-E8-A2JQ-11A TCGA-EL-A3ZT-11A
## ENSG00000187634 8.51 10.46 6.36
## ENSG00000188976 57.23 52.97 52.68
## ENSG00000187961 6.90 3.78 3.89
## ENSG00000187583 5.94 1.19 0.35
## ENSG00000187642 1.90 0.45 0.12
## ENSG00000188290 1.84 1.04 0.69
## TCGA-EL-A3MW-11A TCGA-BJ-A28T-01A TCGA-DO-A1JZ-11A
## ENSG00000187634 9.89 9.05 8.39
## ENSG00000188976 49.79 49.42 69.10
## ENSG00000187961 1.60 2.69 1.70
## ENSG00000187583 0.31 2.19 0.27
## ENSG00000187642 0.06 0.77 0.05
## ENSG00000188290 1.45 1.02 0.64
## TCGA-EL-A3N3-11A TCGA-BJ-A290-11A TCGA-E8-A242-01A
## ENSG00000187634 8.76 8.81 24.61
## ENSG00000188976 52.05 46.19 67.21
## ENSG00000187961 2.44 1.62 3.65
## ENSG00000187583 0.48 0.27 17.28
## ENSG00000187642 0.22 0.07 5.99
## ENSG00000188290 0.96 0.67 4.48
## TCGA-ET-A39L-01A TCGA-EL-A3MY-01A TCGA-EL-A3T8-11A
## ENSG00000187634 13.56 19.41 11.77
## ENSG00000188976 67.56 65.75 52.41
## ENSG00000187961 2.49 5.23 3.89
## ENSG00000187583 3.49 15.41 0.95
## ENSG00000187642 1.37 5.24 0.54
## ENSG00000188290 2.60 10.05 2.74
## TCGA-EM-A1CV-11A TCGA-EM-A1CW-11A TCGA-EL-A3N2-11A
## ENSG00000187634 11.22 3.73 6.65
## ENSG00000188976 72.45 40.22 47.38
## ENSG00000187961 2.37 0.95 1.99
## ENSG00000187583 0.45 0.15 0.51
## ENSG00000187642 0.18 0.00 0.23
## ENSG00000188290 0.45 0.38 0.91
## TCGA-EL-A3T3-11A TCGA-BJ-A45G-01A TCGA-EL-A3CY-01A
## ENSG00000187634 12.64 12.29 13.47
## ENSG00000188976 55.97 58.84 56.38
## ENSG00000187961 3.20 3.74 5.23
## ENSG00000187583 0.27 4.54 4.11
## ENSG00000187642 0.17 1.77 1.51
## ENSG00000188290 1.32 3.98 3.34
## TCGA-EL-A3ZR-11A TCGA-EL-A3T7-11A TCGA-CE-A3ME-01A
## ENSG00000187634 8.19 8.02 23.22
## ENSG00000188976 50.14 52.78 85.82
## ENSG00000187961 3.98 3.63 5.20
## ENSG00000187583 1.43 0.43 6.15
## ENSG00000187642 3.28 0.35 2.50
## ENSG00000188290 3.12 1.08 4.86
## TCGA-EL-A3T6-11A TCGA-KS-A4ID-01A TCGA-ET-A3BP-01A
## ENSG00000187634 16.15 18.25 23.52
## ENSG00000188976 63.30 68.62 67.54
## ENSG00000187961 4.39 5.07 3.97
## ENSG00000187583 1.37 9.35 6.72
## ENSG00000187642 0.39 4.15 3.35
## ENSG00000188290 8.25 8.86 4.34
## TCGA-EL-A3ZG-11A TCGA-ET-A2MX-11C TCGA-BJ-A18Z-01A
## ENSG00000187634 17.86 11.05 17.47
## ENSG00000188976 65.49 57.54 60.89
## ENSG00000187961 3.26 2.95 6.96
## ENSG00000187583 0.21 0.46 13.35
## ENSG00000187642 0.21 0.19 5.96
## ENSG00000188290 6.33 0.97 4.99
## TCGA-GE-A2C6-11A TCGA-BJ-A2N9-11A TCGA-ET-A39N-01A
## ENSG00000187634 7.45 12.69 21.60
## ENSG00000188976 50.85 66.84 89.06
## ENSG00000187961 2.53 3.63 2.63
## ENSG00000187583 0.69 1.10 1.52
## ENSG00000187642 0.45 0.38 0.89
## ENSG00000188290 1.03 1.05 4.04
## TCGA-DJ-A2QC-01A
## ENSG00000187634 59.59
## ENSG00000188976 115.98
## ENSG00000187961 5.86
## ENSG00000187583 11.45
## ENSG00000187642 6.50
## ENSG00000188290 12.30cpm_table_log <- as.data.frame(round(log10(cpm(edge_n)+1),2))
head(cpm_table_log)## TCGA-DJ-A2Q1-01A TCGA-H2-A2K9-11A TCGA-H2-A3RI-11A
## ENSG00000187634 1.43 0.90 0.99
## ENSG00000188976 1.89 1.68 1.69
## ENSG00000187961 0.80 0.56 0.59
## ENSG00000187583 1.12 0.21 0.16
## ENSG00000187642 1.07 0.10 0.06
## ENSG00000188290 0.50 0.21 0.29
## TCGA-DJ-A4V4-01A TCGA-EL-A3T9-01A TCGA-BJ-A0ZB-01A
## ENSG00000187634 1.41 1.27 1.18
## ENSG00000188976 1.88 1.94 1.86
## ENSG00000187961 0.86 1.02 0.62
## ENSG00000187583 1.05 1.18 0.87
## ENSG00000187642 0.81 0.77 0.53
## ENSG00000188290 0.92 0.66 0.62
## TCGA-EL-A3H1-01A TCGA-H2-A3RI-01A TCGA-EL-A3GR-01A
## ENSG00000187634 1.08 1.42 1.19
## ENSG00000188976 1.79 1.86 1.74
## ENSG00000187961 0.63 0.84 0.79
## ENSG00000187583 0.23 0.99 1.04
## ENSG00000187642 0.06 0.58 0.55
## ENSG00000188290 0.66 0.60 0.92
## TCGA-DJ-A3UN-01A TCGA-DO-A1JZ-01A TCGA-EL-A3GU-01A
## ENSG00000187634 1.32 1.55 1.12
## ENSG00000188976 1.94 1.92 1.78
## ENSG00000187961 0.84 0.78 0.76
## ENSG00000187583 1.09 0.80 1.00
## ENSG00000187642 0.76 0.43 0.57
## ENSG00000188290 0.67 0.63 1.15
## TCGA-DJ-A1QM-01A TCGA-EM-A1CT-11A TCGA-EL-A3CZ-01A
## ENSG00000187634 1.24 1.07 0.84
## ENSG00000188976 1.79 1.77 1.70
## ENSG00000187961 0.64 0.46 0.53
## ENSG00000187583 0.77 0.12 0.46
## ENSG00000187642 0.43 0.09 0.19
## ENSG00000188290 0.44 0.39 0.30
## TCGA-EL-A3ZS-11A TCGA-J8-A3YE-01A TCGA-ET-A2N4-01A
## ENSG00000187634 1.48 1.11 1.61
## ENSG00000188976 1.91 1.80 2.01
## ENSG00000187961 0.96 0.99 0.86
## ENSG00000187583 0.42 0.93 1.21
## ENSG00000187642 0.39 0.51 0.88
## ENSG00000188290 1.25 1.03 0.95
## TCGA-EM-A4FO-01A TCGA-EL-A3ZO-11A TCGA-ET-A4KN-01A
## ENSG00000187634 1.33 1.13 2.31
## ENSG00000188976 1.83 1.70 2.38
## ENSG00000187961 0.93 0.60 0.87
## ENSG00000187583 0.95 0.24 1.31
## ENSG00000187642 0.70 0.14 1.46
## ENSG00000188290 0.99 1.07 1.30
## TCGA-EL-A3T1-11A TCGA-ET-A2N5-11B TCGA-BJ-A2N8-11A
## ENSG00000187634 1.11 1.03 0.99
## ENSG00000188976 1.74 1.82 1.80
## ENSG00000187961 0.57 0.54 0.44
## ENSG00000187583 0.32 0.04 0.26
## ENSG00000187642 0.18 0.05 0.10
## ENSG00000188290 0.62 0.27 0.20
## TCGA-FY-A3I4-01A TCGA-EL-A3ZQ-11A TCGA-DJ-A2Q2-01A
## ENSG00000187634 1.36 1.05 1.10
## ENSG00000188976 1.83 1.75 1.86
## ENSG00000187961 0.69 0.76 0.62
## ENSG00000187583 1.09 0.21 0.48
## ENSG00000187642 0.68 0.11 0.22
## ENSG00000188290 0.99 0.47 0.47
## TCGA-EL-A3ZP-11A TCGA-EL-A3GQ-01A TCGA-EL-A3TA-11A
## ENSG00000187634 0.97 1.48 1.04
## ENSG00000188976 1.70 1.80 1.81
## ENSG00000187961 0.68 0.74 0.57
## ENSG00000187583 0.28 0.81 0.11
## ENSG00000187642 0.12 0.46 0.08
## ENSG00000188290 0.52 0.39 0.23
## TCGA-CE-A481-01A TCGA-E8-A438-01A TCGA-EL-A3H7-11A
## ENSG00000187634 1.10 1.80 1.08
## ENSG00000188976 1.78 2.00 1.81
## ENSG00000187961 0.85 1.13 0.56
## ENSG00000187583 0.74 1.33 0.31
## ENSG00000187642 0.46 1.10 0.16
## ENSG00000188290 0.76 1.40 0.58
## TCGA-EL-A3GZ-11A TCGA-E8-A413-01A TCGA-BJ-A28X-11A
## ENSG00000187634 0.98 1.27 1.06
## ENSG00000188976 1.74 1.82 1.74
## ENSG00000187961 0.42 0.86 0.88
## ENSG00000187583 0.08 0.99 0.39
## ENSG00000187642 0.04 0.57 0.13
## ENSG00000188290 0.31 1.23 0.22
## TCGA-EM-A2CS-01A TCGA-ET-A3BX-01A TCGA-DJ-A13V-01A
## ENSG00000187634 1.36 1.64 1.56
## ENSG00000188976 1.87 1.92 1.94
## ENSG00000187961 0.66 0.76 0.73
## ENSG00000187583 0.70 1.32 1.15
## ENSG00000187642 0.37 0.91 0.76
## ENSG00000188290 0.62 0.88 0.80
## TCGA-KS-A41I-11A TCGA-E8-A44K-01A TCGA-ET-A3DW-11A
## ENSG00000187634 1.05 1.11 1.04
## ENSG00000188976 1.77 1.82 1.76
## ENSG00000187961 0.82 0.90 0.45
## ENSG00000187583 0.44 0.98 0.25
## ENSG00000187642 0.23 0.61 0.11
## ENSG00000188290 0.73 0.88 0.46
## TCGA-EL-A3ZL-11A TCGA-EL-A4JV-01A TCGA-FE-A231-01A
## ENSG00000187634 0.98 1.11 0.91
## ENSG00000188976 1.78 1.79 1.82
## ENSG00000187961 0.56 0.72 0.65
## ENSG00000187583 0.20 0.51 0.99
## ENSG00000187642 0.14 0.23 0.55
## ENSG00000188290 0.23 0.86 0.42
## TCGA-BJ-A28R-11A TCGA-EM-A1CS-11A TCGA-FE-A3PA-01A
## ENSG00000187634 1.01 1.14 1.32
## ENSG00000188976 1.71 1.80 1.78
## ENSG00000187961 0.55 0.50 1.09
## ENSG00000187583 0.24 0.12 1.06
## ENSG00000187642 0.08 0.08 1.01
## ENSG00000188290 0.41 0.36 1.36
## TCGA-EM-A2CQ-01A TCGA-EL-A3TB-11A TCGA-BJ-A28W-11A
## ENSG00000187634 1.01 1.12 1.31
## ENSG00000188976 1.82 1.77 1.91
## ENSG00000187961 0.47 0.41 0.69
## ENSG00000187583 0.51 0.05 0.70
## ENSG00000187642 0.21 0.04 0.52
## ENSG00000188290 0.62 0.20 0.90
## TCGA-KS-A41J-11A TCGA-EL-A3T2-11A TCGA-KS-A41L-11A
## ENSG00000187634 0.90 0.94 1.00
## ENSG00000188976 1.70 1.76 1.76
## ENSG00000187961 0.66 0.58 0.75
## ENSG00000187583 0.21 0.10 0.13
## ENSG00000187642 0.06 0.11 0.08
## ENSG00000188290 0.57 0.26 0.47
## TCGA-EL-A3T0-11A TCGA-DJ-A3UP-01A TCGA-DJ-A4V0-01A
## ENSG00000187634 1.00 1.05 1.18
## ENSG00000188976 1.75 1.76 1.75
## ENSG00000187961 0.58 0.71 0.84
## ENSG00000187583 0.13 0.75 0.68
## ENSG00000187642 0.07 0.42 0.33
## ENSG00000188290 0.29 0.30 0.53
## TCGA-EL-A3ZK-11A TCGA-BJ-A3PT-01A TCGA-EM-A3OA-01A
## ENSG00000187634 1.21 1.66 1.22
## ENSG00000188976 1.74 1.92 1.82
## ENSG00000187961 0.79 0.72 0.76
## ENSG00000187583 0.45 0.78 0.36
## ENSG00000187642 0.24 0.59 0.14
## ENSG00000188290 0.36 0.67 0.82
## TCGA-FY-A3TY-11A TCGA-EL-A3MY-11A TCGA-FY-A3R9-01A
## ENSG00000187634 0.97 1.17 1.18
## ENSG00000188976 1.73 1.71 1.78
## ENSG00000187961 0.70 0.55 0.57
## ENSG00000187583 0.10 0.21 0.34
## ENSG00000187642 0.07 0.07 0.16
## ENSG00000188290 0.34 0.58 0.39
## TCGA-BJ-A3PR-11A TCGA-EL-A3H2-11A TCGA-ET-A2MY-01A
## ENSG00000187634 1.72 0.99 1.50
## ENSG00000188976 2.01 1.74 1.96
## ENSG00000187961 0.81 0.42 0.66
## ENSG00000187583 0.34 0.10 0.99
## ENSG00000187642 0.37 0.02 0.67
## ENSG00000188290 0.74 0.16 0.62
## TCGA-ET-A3DP-11A TCGA-L6-A4ET-01A TCGA-BJ-A3PU-11A
## ENSG00000187634 0.97 1.70 1.09
## ENSG00000188976 1.78 2.03 1.77
## ENSG00000187961 0.24 1.06 0.57
## ENSG00000187583 0.07 1.26 0.15
## ENSG00000187642 0.03 0.84 0.08
## ENSG00000188290 0.34 1.03 0.33
## TCGA-DO-A2HM-01B TCGA-E8-A2JQ-11A TCGA-EL-A3ZT-11A
## ENSG00000187634 0.98 1.06 0.87
## ENSG00000188976 1.77 1.73 1.73
## ENSG00000187961 0.90 0.68 0.69
## ENSG00000187583 0.84 0.34 0.13
## ENSG00000187642 0.46 0.16 0.05
## ENSG00000188290 0.45 0.31 0.23
## TCGA-EL-A3MW-11A TCGA-BJ-A28T-01A TCGA-DO-A1JZ-11A
## ENSG00000187634 1.04 1.00 0.97
## ENSG00000188976 1.71 1.70 1.85
## ENSG00000187961 0.41 0.57 0.43
## ENSG00000187583 0.12 0.50 0.10
## ENSG00000187642 0.02 0.25 0.02
## ENSG00000188290 0.39 0.31 0.22
## TCGA-EL-A3N3-11A TCGA-BJ-A290-11A TCGA-E8-A242-01A
## ENSG00000187634 0.99 0.99 1.41
## ENSG00000188976 1.72 1.67 1.83
## ENSG00000187961 0.54 0.42 0.67
## ENSG00000187583 0.17 0.10 1.26
## ENSG00000187642 0.09 0.03 0.84
## ENSG00000188290 0.29 0.22 0.74
## TCGA-ET-A39L-01A TCGA-EL-A3MY-01A TCGA-EL-A3T8-11A
## ENSG00000187634 1.16 1.31 1.11
## ENSG00000188976 1.84 1.82 1.73
## ENSG00000187961 0.54 0.79 0.69
## ENSG00000187583 0.65 1.22 0.29
## ENSG00000187642 0.38 0.80 0.19
## ENSG00000188290 0.56 1.04 0.57
## TCGA-EM-A1CV-11A TCGA-EM-A1CW-11A TCGA-EL-A3N2-11A
## ENSG00000187634 1.09 0.68 0.88
## ENSG00000188976 1.87 1.62 1.68
## ENSG00000187961 0.53 0.29 0.48
## ENSG00000187583 0.16 0.06 0.18
## ENSG00000187642 0.07 0.00 0.09
## ENSG00000188290 0.16 0.14 0.28
## TCGA-EL-A3T3-11A TCGA-BJ-A45G-01A TCGA-EL-A3CY-01A
## ENSG00000187634 1.13 1.12 1.16
## ENSG00000188976 1.76 1.78 1.76
## ENSG00000187961 0.62 0.68 0.79
## ENSG00000187583 0.11 0.74 0.71
## ENSG00000187642 0.07 0.44 0.40
## ENSG00000188290 0.37 0.70 0.64
## TCGA-EL-A3ZR-11A TCGA-EL-A3T7-11A TCGA-CE-A3ME-01A
## ENSG00000187634 0.96 0.96 1.38
## ENSG00000188976 1.71 1.73 1.94
## ENSG00000187961 0.70 0.67 0.79
## ENSG00000187583 0.39 0.15 0.85
## ENSG00000187642 0.63 0.13 0.54
## ENSG00000188290 0.61 0.32 0.77
## TCGA-EL-A3T6-11A TCGA-KS-A4ID-01A TCGA-ET-A3BP-01A
## ENSG00000187634 1.23 1.28 1.39
## ENSG00000188976 1.81 1.84 1.84
## ENSG00000187961 0.73 0.78 0.70
## ENSG00000187583 0.38 1.01 0.89
## ENSG00000187642 0.14 0.71 0.64
## ENSG00000188290 0.97 0.99 0.73
## TCGA-EL-A3ZG-11A TCGA-ET-A2MX-11C TCGA-BJ-A18Z-01A
## ENSG00000187634 1.28 1.08 1.27
## ENSG00000188976 1.82 1.77 1.79
## ENSG00000187961 0.63 0.60 0.90
## ENSG00000187583 0.08 0.16 1.16
## ENSG00000187642 0.08 0.07 0.84
## ENSG00000188290 0.87 0.29 0.78
## TCGA-GE-A2C6-11A TCGA-BJ-A2N9-11A TCGA-ET-A39N-01A
## ENSG00000187634 0.93 1.14 1.35
## ENSG00000188976 1.71 1.83 1.95
## ENSG00000187961 0.55 0.67 0.56
## ENSG00000187583 0.23 0.32 0.40
## ENSG00000187642 0.16 0.14 0.28
## ENSG00000188290 0.31 0.31 0.70
## TCGA-DJ-A2QC-01A
## ENSG00000187634 1.78
## ENSG00000188976 2.07
## ENSG00000187961 0.84
## ENSG00000187583 1.10
## ENSG00000187642 0.87
## ENSG00000188290 1.12We can now define the design to perform the DE analysis. We build a model without the intercept.
desing <- model.matrix(~0+group, data = edge_n$samples) # group correspond to the data
colnames(desing) <- levels(edge_n$samples$group)
rownames(desing) <- edge_n$samples$sample
desing## case control
## TCGA-DJ-A2Q1-01A 1 0
## TCGA-H2-A2K9-11A 0 1
## TCGA-H2-A3RI-11A 0 1
## TCGA-DJ-A4V4-01A 1 0
## TCGA-EL-A3T9-01A 1 0
## TCGA-BJ-A0ZB-01A 1 0
## TCGA-EL-A3H1-01A 1 0
## TCGA-H2-A3RI-01A 1 0
## TCGA-EL-A3GR-01A 1 0
## TCGA-DJ-A3UN-01A 1 0
## TCGA-DO-A1JZ-01A 1 0
## TCGA-EL-A3GU-01A 1 0
## TCGA-DJ-A1QM-01A 1 0
## TCGA-EM-A1CT-11A 0 1
## TCGA-EL-A3CZ-01A 1 0
## TCGA-EL-A3ZS-11A 0 1
## TCGA-J8-A3YE-01A 1 0
## TCGA-ET-A2N4-01A 1 0
## TCGA-EM-A4FO-01A 1 0
## TCGA-EL-A3ZO-11A 0 1
## TCGA-ET-A4KN-01A 1 0
## TCGA-EL-A3T1-11A 0 1
## TCGA-ET-A2N5-11B 0 1
## TCGA-BJ-A2N8-11A 0 1
## TCGA-FY-A3I4-01A 1 0
## TCGA-EL-A3ZQ-11A 0 1
## TCGA-DJ-A2Q2-01A 1 0
## TCGA-EL-A3ZP-11A 0 1
## TCGA-EL-A3GQ-01A 1 0
## TCGA-EL-A3TA-11A 0 1
## TCGA-CE-A481-01A 1 0
## TCGA-E8-A438-01A 1 0
## TCGA-EL-A3H7-11A 0 1
## TCGA-EL-A3GZ-11A 0 1
## TCGA-E8-A413-01A 1 0
## TCGA-BJ-A28X-11A 0 1
## TCGA-EM-A2CS-01A 1 0
## TCGA-ET-A3BX-01A 1 0
## TCGA-DJ-A13V-01A 1 0
## TCGA-KS-A41I-11A 0 1
## TCGA-E8-A44K-01A 1 0
## TCGA-ET-A3DW-11A 0 1
## TCGA-EL-A3ZL-11A 0 1
## TCGA-EL-A4JV-01A 1 0
## TCGA-FE-A231-01A 1 0
## TCGA-BJ-A28R-11A 0 1
## TCGA-EM-A1CS-11A 0 1
## TCGA-FE-A3PA-01A 1 0
## TCGA-EM-A2CQ-01A 1 0
## TCGA-EL-A3TB-11A 0 1
## TCGA-BJ-A28W-11A 0 1
## TCGA-KS-A41J-11A 0 1
## TCGA-EL-A3T2-11A 0 1
## TCGA-KS-A41L-11A 0 1
## TCGA-EL-A3T0-11A 0 1
## TCGA-DJ-A3UP-01A 1 0
## TCGA-DJ-A4V0-01A 1 0
## TCGA-EL-A3ZK-11A 0 1
## TCGA-BJ-A3PT-01A 1 0
## TCGA-EM-A3OA-01A 1 0
## TCGA-FY-A3TY-11A 0 1
## TCGA-EL-A3MY-11A 0 1
## TCGA-FY-A3R9-01A 1 0
## TCGA-BJ-A3PR-11A 0 1
## TCGA-EL-A3H2-11A 0 1
## TCGA-ET-A2MY-01A 1 0
## TCGA-ET-A3DP-11A 0 1
## TCGA-L6-A4ET-01A 1 0
## TCGA-BJ-A3PU-11A 0 1
## TCGA-DO-A2HM-01B 1 0
## TCGA-E8-A2JQ-11A 0 1
## TCGA-EL-A3ZT-11A 0 1
## TCGA-EL-A3MW-11A 0 1
## TCGA-BJ-A28T-01A 1 0
## TCGA-DO-A1JZ-11A 0 1
## TCGA-EL-A3N3-11A 0 1
## TCGA-BJ-A290-11A 0 1
## TCGA-E8-A242-01A 1 0
## TCGA-ET-A39L-01A 1 0
## TCGA-EL-A3MY-01A 1 0
## TCGA-EL-A3T8-11A 0 1
## TCGA-EM-A1CV-11A 0 1
## TCGA-EM-A1CW-11A 0 1
## TCGA-EL-A3N2-11A 0 1
## TCGA-EL-A3T3-11A 0 1
## TCGA-BJ-A45G-01A 1 0
## TCGA-EL-A3CY-01A 1 0
## TCGA-EL-A3ZR-11A 0 1
## TCGA-EL-A3T7-11A 0 1
## TCGA-CE-A3ME-01A 1 0
## TCGA-EL-A3T6-11A 0 1
## TCGA-KS-A4ID-01A 1 0
## TCGA-ET-A3BP-01A 1 0
## TCGA-EL-A3ZG-11A 0 1
## TCGA-ET-A2MX-11C 0 1
## TCGA-BJ-A18Z-01A 1 0
## TCGA-GE-A2C6-11A 0 1
## TCGA-BJ-A2N9-11A 0 1
## TCGA-ET-A39N-01A 1 0
## TCGA-DJ-A2QC-01A 1 0
## attr(,"assign")
## [1] 1 1
## attr(,"contrasts")
## attr(,"contrasts")$group
## [1] "contr.treatment"Then, we estimate the dispersion providing to the command estimateDisp the object containing the normalized factors and the design. The obtained object is used to fit the data.
edge_d <- estimateDisp(edge_n,design = desing)
View(edge_d)We can now fit the data using glmQLFIT to retrieve the p-values. The data is modeled using a negative binomial distribution.
edge_f <- glmQLFit(edge_d,design = desing)
View(edge_f)## Warning in (function (..., row.names = NULL, check.rows = FALSE, check.names =
## TRUE, : row names were found from a short variable and have been discarded## Warning in format.data.frame(x0): corrupt data frame: columns will be truncated
## or padded with NAsWe define the contrast, which corresponds to the conditions to be compared.
contro <- makeContrasts('case-control', levels = desing)We fit again the model using the contrast created.
edge_t <- glmQLFTest(edge_f,contrast = contro) # contrin the results of the DE Analysis
View(edge_t)We sort the result by fold change by using the function TopTags and expressing a cutoff and sorting.
DEGs <- as.data.frame(topTags(edge_t,n = 16748 ,p.value = 0.01,sort.by = 'logFC')) # 16748 because we took into consideration all the variables inside edge_t, so 99 percent of singificativity of the test
View(DEGs)We add a new column to the DEGs dataframe called class. Used to express the values of the fold change of the transcripts. The selection is based on the log fold change ratio (>1.5 for up-regulated genes and < (-1.5) for down-regulated genes) and a log CPM (>1 for both cases). From the contingency table of our DEGs we can see that the up regulated genes correspond to the 7% of the total and the down regulated are the 2.8% of the total.
DEGs$class <- '='
DEGs$class[which(DEGs$logCPM > 1 & DEGs$logFC > 1.5)] = '+'
DEGs$class[which(DEGs$logCPM > 1 & DEGs$logFC < (-1.5))] = '-'
DEGs <- DEGs[order(DEGs$logFC, decreasing = T),] # we order based on the fold change
View(DEGs)
table(DEGs$class)##
## - + =
## 245 618 7927Display the results using a volcano plot (x-axes: log FoldChange, y-axes: inverse function of the p-value). We can see the most significant DEGs colored in green, which are genes that surpass a threshold set on both the p-value and the Fold Change. We can see that there are a lot of DEGs, both up (right side of the plot) or down (left side of the plot) expressed. We have also a confirmation of teh fact that we have more DEGs upregulated compared to the down regulated.
input_df<-DEGs
xlabel<- "log2 FC control vs case"
ylabel<-"-log10 p-value"
par(fig=c(0,1, 0,1), mar=c(4,4,1,2), mgp=c(2, 0.75,0))
plot(DEGs$logFC,-log(DEGs$PValue, base=10), xlab=xlabel,ylab = ylabel, col=ifelse(DEGs$class=="=", "grey70", "olivedrab4"), pch=20, frame.plot=TRUE, cex=0.8, main="Volcano plot") %>%
abline(v = 0, lty = 2, col="grey20")
We can also represent the genes using a heatmap. A clustering process is operated. We plot only up or down expressed genes using data from both the normalized CPM and the log transformation of the CPM table. We can see that thanks to clusterization, chunks of expressed genes are outlined. Specifically, in the case of the CPM log table, a more refinement clusterization is observed, and minor contamination at the level of the division between “case” samples and “control” samples is observed. We need to remember that contamination that took place during the collection of tissue samples is able to influence the clustering process. From both plots a clear difference in expression between tumor and control samples is observed, indicating that the expression of the genes differs between the two cases, as expected since we are performing a DEGs analysis.
col <- rep('chartreuse4', 100)
col[which(c_anno_df$condition == 'case')] <- 'burlywood3'
pal <- c('blue','white','red')
pal <- colorRampPalette(pal)(100)
heatmap(as.matrix(cpm_table[which(rownames(cpm_table) %in% DEGs$ensembl_gene_id[which(DEGs$class != '=')]),]),ColSideColors = col, cexCol = 0.5,margins = c(4,4), col = pal, cexRow = 0.2) 
heatmap(as.matrix(cpm_table_log[which(rownames(cpm_table_log) %in% DEGs$ensembl_gene_id[which(DEGs$class != '=')]),]),ColSideColors = col, cexCol = 0.5, margins = c(4,4), col = pal, cexRow = 0.2) 
- Perform gene set enrichment analysis using clusterProfiler R package.
- Perform both GO (BP and MF) and WP analysis
- Report the top 10 enriched GO terms and the top 10 enriched WP pathways resulting from both up- and down-regulated gene lists
library(clusterProfiler)## ## clusterProfiler v4.8.1 For help: https://yulab-smu.top/biomedical-knowledge-mining-book/
##
## If you use clusterProfiler in published research, please cite:
## T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141##
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## select## We create the convert dataframe, that is used to map the ensembl_gene_id to eneterzgene_id and the external_gene_name
convert<-getBM(attributes=c("ensembl_gene_id", "entrezgene_id", "external_gene_name"), filter=c("ensembl_gene_id"), values=DEGs$ensembl_gene_id, mart=ensembl)We add the information of convert in the initial file of DEGs by using the command merge.
DEGs<-merge(DEGs, convert, by.x = "ensembl_gene_id", by.y = "ensembl_gene_id")Then we remove the NA and the duplicates inside the new DEGs.
DEGs<-DEGs[which(!is.na(DEGs$entrezgene_id)),]
DEGs<-DEGs[-which(duplicated(DEGs$entrezgene_id)),]We create a new list of only the up-regulated genes.
UPDegs <- DEGs %>% filter(class == '+') # up regulated Perform Gene Ontology enrichment analysis (biological process). We use the function enrichGO by providing a list of symbols of up-regulated genes and the database of the human species (=gene model). Then we explicit the wanted ontology, here BP for biological process and the methods to adjust the p-value. The barplot and the dotplot return us the counts of top 10 enriched terms based on the p-value. The heatplot shows us the genes associated to the top two enriched terms, in this case seems all the genes are shared between the two terms except for TGM1.
ego_BP_UP <- enrichGO(gene = UPDegs$external_gene_name.x, OrgDb = org.Hs.eg.db, keyType = 'SYMBOL', ont = 'BP',pAdjustMethod = 'BH',pvalueCutoff = 0.05, qvalueCutoff = 0.05)
View(ego_BP_UP)
barplot(ego_BP_UP,showCategory = 10) # first ten enriched terms 
dotplot(ego_BP_UP, showCategory= 10) # first ten enriched terms 
heatplot(ego_BP_UP, showCategory = 2) # gene associated with the top two enriched terms 
head(ego_BP_UP,10)## ID Description
## GO:0045229 GO:0045229 external encapsulating structure organization
## GO:0030198 GO:0030198 extracellular matrix organization
## GO:0043062 GO:0043062 extracellular structure organization
## GO:0030199 GO:0030199 collagen fibril organization
## GO:0050808 GO:0050808 synapse organization
## GO:0043588 GO:0043588 skin development
## GO:0034329 GO:0034329 cell junction assembly
## GO:0098742 GO:0098742 cell-cell adhesion via plasma-membrane adhesion molecules
## GO:0042060 GO:0042060 wound healing
## GO:0051960 GO:0051960 regulation of nervous system development
## GeneRatio BgRatio pvalue p.adjust qvalue
## GO:0045229 43/559 317/18614 1.028456e-16 4.371965e-13 3.746826e-13
## GO:0030198 42/559 314/18614 3.944334e-16 6.271993e-13 5.375173e-13
## GO:0043062 42/559 315/18614 4.426248e-16 6.271993e-13 5.375173e-13
## GO:0030199 18/559 64/18614 3.008218e-13 3.196984e-10 2.739853e-10
## GO:0050808 43/559 466/18614 6.797067e-11 5.778866e-08 4.952557e-08
## GO:0043588 32/559 308/18614 1.089958e-09 7.722353e-07 6.618149e-07
## GO:0034329 39/559 444/18614 2.220585e-09 1.348530e-06 1.155706e-06
## GO:0098742 29/559 278/18614 6.070815e-09 3.225879e-06 2.764617e-06
## GO:0042060 37/559 439/18614 1.738908e-08 8.213444e-06 7.039020e-06
## GO:0051960 38/559 461/18614 2.015553e-08 8.568115e-06 7.342977e-06
## geneID
## GO:0045229 COL11A1/FAP/ADAMTS2/TGM1/MMP11/COMP/HPN/TGFBR1/SPOCK2/COL1A1/ADTRP/COL7A1/QSOX1/TGFBI/COL10A1/PXDN/COL5A1/POSTN/LOXL2/ADAMTS7/MMP7/CYP1B1/LOXL4/ADAMTS14/ADAMTS12/MMP16/RUNX1/ELF3/ADAMTS9/COL1A2/SPINT1/BMP1/COL3A1/KLK7/COL8A2/TPSAB1/TMPRSS6/COL13A1/DPP4/COL5A2/PRSS1/ZNF469/PRSS2
## GO:0030198 COL11A1/FAP/ADAMTS2/MMP11/COMP/HPN/TGFBR1/SPOCK2/COL1A1/ADTRP/COL7A1/QSOX1/TGFBI/COL10A1/PXDN/COL5A1/POSTN/LOXL2/ADAMTS7/MMP7/CYP1B1/LOXL4/ADAMTS14/ADAMTS12/MMP16/RUNX1/ELF3/ADAMTS9/COL1A2/SPINT1/BMP1/COL3A1/KLK7/COL8A2/TPSAB1/TMPRSS6/COL13A1/DPP4/COL5A2/PRSS1/ZNF469/PRSS2
## GO:0043062 COL11A1/FAP/ADAMTS2/MMP11/COMP/HPN/TGFBR1/SPOCK2/COL1A1/ADTRP/COL7A1/QSOX1/TGFBI/COL10A1/PXDN/COL5A1/POSTN/LOXL2/ADAMTS7/MMP7/CYP1B1/LOXL4/ADAMTS14/ADAMTS12/MMP16/RUNX1/ELF3/ADAMTS9/COL1A2/SPINT1/BMP1/COL3A1/KLK7/COL8A2/TPSAB1/TMPRSS6/COL13A1/DPP4/COL5A2/PRSS1/ZNF469/PRSS2
## GO:0030199 COL11A1/ADAMTS2/MMP11/COMP/TGFBR1/COL1A1/PXDN/COL5A1/LOXL2/ADAMTS7/CYP1B1/LOXL4/ADAMTS14/ADAMTS12/COL1A2/BMP1/COL3A1/COL5A2
## GO:0050808 SEMA3F/PLXND1/ITGA3/TNC/CBLN4/LZTS1/NGEF/CACNB1/NRCAM/PALM/CBLN1/ICAM5/SPOCK2/GLRB/CDH6/NRP2/C3/APOE/DLG4/LRP4/PLXNC1/AMIGO2/NTRK3/TMEM108/GABRB2/SDK1/SEMA3E/CDH2/GAP43/SPTBN2/SEZ6L2/DOK7/SLITRK4/F2R/EPHB3/THBS2/SLIT1/SHISA6/LRRK2/IL1RAP/PLXNA4/ELFN1/SRCIN1
## GO:0043588 ITGA3/CDH3/NGFR/TP63/ADAMTS2/TGM1/COMP/MET/COL1A1/KRT17/COL5A1/ITGB4/LRP4/WNT10A/SCEL/POU2F3/CD109/GRHL3/IVL/CLDN1/ITGA2/FOXQ1/COL1A2/KRT80/COL3A1/SFN/ETV4/ALOX15B/GJB3/RYR1/COL5A2/CDSN
## GO:0034329 PLXND1/CBLN4/PKP2/LZTS1/CDH3/NRCAM/CBLN1/ICAM5/SPOCK2/CDH6/CLDN16/FN1/SDC4/ITGB4/LRP4/APLNR/CLDN10/AMIGO2/NTRK3/CDH11/CDH13/GABRB2/SDK1/CDH22/CLDN1/ITGA2/CLDN2/CDH2/GAP43/SPTBN2/CDH4/SLITRK4/EPHB3/GJC1/THBS2/SLIT1/GJA4/IL1RAP/CLDN9
## GO:0098742 ITGA3/TENM1/CDH3/CEACAM6/FAT2/FXYD5/ICAM1/CBLN1/MAG/CDH6/CLDN16/DSC3/CLDN10/AMIGO2/CDH11/CDH13/NECTIN4/SDK1/MPZL2/CDH22/CLDN1/CLDN2/RET/CDH2/CDH4/IL1RAP/DCHS2/CD177/CLDN9
## GO:0042060 TNFRSF12A/PLAUR/ALOX5/ENTPD2/CDH3/ERBB3/RAB27A/FAP/TYRO3/TIMP1/CCN4/COMP/TGFBR1/COL1A1/ADTRP/FGF1/PLAU/SDC4/APOE/COL5A1/EMILIN2/ENTPD1/CD109/GRHL3/TGFA/CLDN1/ITGA2/COL3A1/LRG1/SAA1/F2R/ADRA2C/PROS1/SERPINA1/F5/TAFA5/EPPK1
## GO:0051960 SEMA3F/PLXND1/TRPC5/BRINP1/E2F1/CBLN1/MAG/WNT3/CTSC/MDK/FN1/MYRF/NR1D1/DLG4/BMAL1/LRP4/HEY2/PLXNC1/AMIGO2/NTRK3/RXRG/CRABP2/HES6/TNFRSF21/CDKN2B/TIAM1/SPINT1/SEMA6B/SEMA3E/CDH4/SLITRK4/PLAG1/EPHB3/THBS2/SLIT1/IL1RAP/TGM2/PLXNA4
## Count
## GO:0045229 43
## GO:0030198 42
## GO:0043062 42
## GO:0030199 18
## GO:0050808 43
## GO:0043588 32
## GO:0034329 39
## GO:0098742 29
## GO:0042060 37
## GO:0051960 38Perform Gene Ontology enrichment analysis (molecular function). We use the function enrichGO by providing a list of symbols of up-regulated genes and the database of the human species (=gene model). Then we explicit the wanted ontology, here MF for molecule function and the methods to adjust the p-value. The barplot and the dotplot return us the counts of top 10 enriched terms based on the p-value. The heatplot shows us the genes associated to the top two enriched terms, we can see that there are not shared genes, indeed they are part of different molecular functions.
ego_MF_UP <- enrichGO(gene = UPDegs$external_gene_name.x, OrgDb = org.Hs.eg.db, keyType = 'SYMBOL', ont = 'MF',pAdjustMethod = 'BH',pvalueCutoff = 0.05, qvalueCutoff = 0.05)
View(ego_MF_UP)
barplot(ego_MF_UP,showCategory = 10) # first ten enriched terms 
dotplot(ego_MF_UP, showCategory= 10 ) # first ten enriched terms 
heatplot(ego_MF_UP, showCategory = 2) # gene associated with the top two enriched terms 
head(ego_MF_UP,10)## ID
## GO:0005201 GO:0005201
## GO:0004252 GO:0004252
## GO:0008236 GO:0008236
## GO:0017171 GO:0017171
## GO:0005178 GO:0005178
## GO:0008201 GO:0008201
## GO:0005539 GO:0005539
## GO:0019838 GO:0019838
## GO:0030020 GO:0030020
## GO:0004175 GO:0004175
## Description
## GO:0005201 extracellular matrix structural constituent
## GO:0004252 serine-type endopeptidase activity
## GO:0008236 serine-type peptidase activity
## GO:0017171 serine hydrolase activity
## GO:0005178 integrin binding
## GO:0008201 heparin binding
## GO:0005539 glycosaminoglycan binding
## GO:0019838 growth factor binding
## GO:0030020 extracellular matrix structural constituent conferring tensile strength
## GO:0004175 endopeptidase activity
## GeneRatio BgRatio pvalue p.adjust qvalue
## GO:0005201 29/568 167/18369 3.738783e-14 2.587238e-11 2.290497e-11
## GO:0004252 24/568 170/18369 5.317169e-10 1.839741e-07 1.628733e-07
## GO:0008236 24/568 190/18369 5.196659e-09 1.168591e-06 1.034560e-06
## GO:0017171 24/568 194/18369 7.887344e-09 1.168591e-06 1.034560e-06
## GO:0005178 21/568 151/18369 8.443578e-09 1.168591e-06 1.034560e-06
## GO:0008201 22/568 168/18369 1.159636e-08 1.337447e-06 1.184050e-06
## GO:0005539 26/568 232/18369 1.525165e-08 1.507735e-06 1.334806e-06
## GO:0019838 18/568 132/18369 1.330448e-07 1.150838e-05 1.018843e-05
## GO:0030020 10/568 41/18369 3.488460e-07 2.682238e-05 2.374601e-05
## GO:0004175 34/568 428/18369 5.318010e-07 3.680063e-05 3.257981e-05
## geneID
## GO:0005201 VCAN/TNC/LTBP1/COL11A1/COMP/COL1A1/COL7A1/FN1/MFAP2/TGFBI/COL10A1/PXDN/COL5A1/EMILIN2/CHI3L1/POSTN/MFGE8/ECM1/HAPLN1/EDIL3/COL1A2/CTHRC1/COL3A1/COL8A2/THBS2/LAMB3/COL13A1/COL5A2/SPON1
## GO:0004252 FAP/MMP11/CTSH/HPN/CTSC/PLAU/PCSK2/KLK10/TMPRSS4/MMP7/PRSS23/PRSS12/KLK6/KLK11/BMP1/KLK7/TPSAB1/TMPRSS6/TPSB2/DPP4/PRSS1/CFI/HP/PRSS2
## GO:0008236 FAP/MMP11/CTSH/HPN/CTSC/PLAU/PCSK2/KLK10/TMPRSS4/MMP7/PRSS23/PRSS12/KLK6/KLK11/BMP1/KLK7/TPSAB1/TMPRSS6/TPSB2/DPP4/PRSS1/CFI/HP/PRSS2
## GO:0017171 FAP/MMP11/CTSH/HPN/CTSC/PLAU/PCSK2/KLK10/TMPRSS4/MMP7/PRSS23/PRSS12/KLK6/KLK11/BMP1/KLK7/TPSAB1/TMPRSS6/TPSB2/DPP4/PRSS1/CFI/HP/PRSS2
## GO:0005178 ITGA3/FAP/ICAM1/CCN4/ICAM4/ICAM5/COMP/THBS4/FGF1/FN1/TGFBI/COL5A1/ITGB4/ITGA11/MFGE8/ITGA2/EDIL3/ESM1/COL3A1/FRMD5/CD177
## GO:0008201 AOC1/TENM1/COL11A1/RSPO4/CCN4/COMP/MDK/THBS4/FGF1/FN1/NRP2/APOE/COL5A1/POSTN/FGFBP1/LIPH/CXCL8/SAA1/LPL/THBS2/SLIT1/COL13A1
## GO:0005539 AOC1/TENM1/VCAN/COL11A1/RSPO4/CCN4/COMP/NOD1/SPOCK2/MDK/THBS4/FGF1/FN1/NRP2/APOE/COL5A1/POSTN/FGFBP1/HAPLN1/LIPH/CXCL8/SAA1/LPL/THBS2/SLIT1/COL13A1
## GO:0019838 LTBP1/NGFR/ERBB3/TGFBR1/COL1A1/IGFBP2/NRP2/PXDN/COL5A1/ITGB4/KL/FGFBP1/NTRK3/IGFBP3/CD109/COL1A2/IGFBP6/COL3A1
## GO:0030020 COL11A1/COL1A1/COL7A1/COL10A1/COL5A1/COL1A2/COL3A1/COL8A2/COL13A1/COL5A2
## GO:0004175 FAP/ADAMTS2/MMP11/PHEX/CTSH/HPN/CTSC/ECE1/PLAU/PCSK2/KLK10/NAPSA/ADAMTS7/TMPRSS4/MMP7/ADAMTS14/ADAM12/PRSS23/ADAMTS12/MMP16/ADAMTS9/PRSS12/KLK6/KLK11/BMP1/KLK7/TPSAB1/TMPRSS6/TPSB2/DPP4/PRSS1/CFI/HP/PRSS2
## Count
## GO:0005201 29
## GO:0004252 24
## GO:0008236 24
## GO:0017171 24
## GO:0005178 21
## GO:0008201 22
## GO:0005539 26
## GO:0019838 18
## GO:0030020 10
## GO:0004175 34We perform KEGG enrichment analysis. We use function enrichWP to retrieve the list of genes from the wiki pathways, we can see which pathways are more expressed.
library(pathview)## ##############################################################################
## Pathview is an open source software package distributed under GNU General
## Public License version 3 (GPLv3). Details of GPLv3 is available at
## http://www.gnu.org/licenses/gpl-3.0.html. Particullary, users are required to
## formally cite the original Pathview paper (not just mention it) in publications
## or products. For details, do citation("pathview") within R.
##
## The pathview downloads and uses KEGG data. Non-academic uses may require a KEGG
## license agreement (details at http://www.kegg.jp/kegg/legal.html).
## ##############################################################################eWP_UP<-enrichWP(gene=UPDegs$entrezgene_id, organism ='Homo sapiens',pvalueCutoff = 0.05, qvalueCutoff = 0.1)
head(eWP_UP, 10)## ID
## WP2877 WP2877
## WP5055 WP5055
## WP4239 WP4239
## WP558 WP558
## WP4541 WP4541
## WP3859 WP3859
## WP5078 WP5078
## WP3967 WP3967
## WP1742 WP1742
## WP3624 WP3624
## Description
## WP2877 Vitamin D receptor pathway
## WP5055 Burn wound healing
## WP4239 Epithelial to mesenchymal transition in colorectal cancer
## WP558 Complement and coagulation cascades
## WP4541 Hippo-Merlin signaling dysregulation
## WP3859 TGF-beta signaling in thyroid cells for epithelial-mesenchymal transition
## WP5078 T cell modulation in pancreatic cancer
## WP3967 miR-509-3p alteration of YAP1/ECM axis
## WP1742 TP53 network
## WP3624 Lung fibrosis
## GeneRatio BgRatio pvalue p.adjust qvalue
## WP2877 23/344 187/8442 1.938171e-06 0.0008140316 0.0007507861
## WP5055 16/344 113/8442 1.229291e-05 0.0025815119 0.0023809433
## WP4239 18/344 162/8442 9.987943e-05 0.0105809630 0.0097588832
## WP558 10/344 58/8442 1.007711e-04 0.0105809630 0.0097588832
## WP4541 14/344 121/8442 3.867063e-04 0.0282339716 0.0260403547
## WP3859 5/344 17/8442 4.501618e-04 0.0282339716 0.0260403547
## WP5078 8/344 46/8442 4.705662e-04 0.0282339716 0.0260403547
## WP3967 5/344 18/8442 6.026636e-04 0.0316398364 0.0291816035
## WP1742 5/344 19/8442 7.908468e-04 0.0369061850 0.0340387871
## WP3624 9/344 64/8442 1.058800e-03 0.0444695799 0.0410145499
## geneID
## WP2877 240/1591/639/595/50486/54210/9365/3486/1030/1029/10568/6280/9075/29785/5653/147/1469/1474/283229/6275/6273/1305/6277
## WP5055 3371/50507/3383/3956/7076/10392/1277/2335/8796/9547/8038/6282/6280/1278/3576/6277
## WP4239 5318/7991/7046/101929777/7473/10686/2335/8828/9518/9071/80326/56649/9076/94234/9075/1000/3714/9080
## WP558 5329/1191/5328/718/2149/5627/1604/5265/2153/3426
## WP4541 3675/1001/4804/4233/595/1004/3691/22801/1009/1012/64405/3673/1000/1002
## WP3859 3371/1004/2335/860/1000
## WP5078 2191/3956/6367/6361/3958/79679/4907/953
## WP3967 1277/2335/1289/1281/7058
## WP1742 637/8626/27113/5366/1029
## WP3624 7076/2246/653509/5328/57105/7039/3576/729238/5265
## Count
## WP2877 23
## WP5055 16
## WP4239 18
## WP558 10
## WP4541 14
## WP3859 5
## WP5078 8
## WP3967 5
## WP1742 5
## WP3624 9The most expressed one is the Vitamin D receptor pathway.
Then we proceed with the same processes but for down-regulated: so we create a new list of only the down-regulated genes. We needed to change the thresholds value in all the following analysis. For the enrichGO the set thresholds are still such to obtain good and significative results. While for the KEGG analysis we get p-value adjusted higher than 0.1 indicating that the results can’t be significative.
DWDegs <- DEGs %>% filter(class == '-')
# biologica process of GO analysis
ego_BP_DW <- enrichGO(gene = DWDegs$external_gene_name.x, OrgDb = org.Hs.eg.db, keyType = 'SYMBOL', ont = 'BP',pAdjustMethod = 'BH',pvalueCutoff = 0.1, qvalueCutoff = 0.1)
View(ego_BP_DW)
barplot(ego_BP_DW,showCategory = 10)
dotplot(ego_BP_DW, showCategory= 10) # orderby default x 
heatplot(ego_BP_DW, showCategory = 2)
head(ego_BP_DW,10)## ID Description GeneRatio
## GO:0030198 GO:0030198 extracellular matrix organization 14/217
## GO:0043062 GO:0043062 extracellular structure organization 14/217
## GO:0045229 GO:0045229 external encapsulating structure organization 14/217
## GO:0015671 GO:0015671 oxygen transport 4/217
## GO:0019755 GO:0019755 one-carbon compound transport 6/217
## GO:0001503 GO:0001503 ossification 16/217
## GO:0015669 GO:0015669 gas transport 4/217
## GO:0097553 GO:0097553 calcium ion transmembrane import into cytosol 10/217
## GO:0061564 GO:0061564 axon development 16/217
## GO:0031214 GO:0031214 biomineral tissue development 9/217
## BgRatio pvalue p.adjust qvalue
## GO:0030198 314/18614 2.014588e-05 0.02166984 0.01930285
## GO:0043062 315/18614 2.086855e-05 0.02166984 0.01930285
## GO:0045229 317/18614 2.238273e-05 0.02166984 0.01930285
## GO:0015671 16/18614 2.929346e-05 0.02166984 0.01930285
## GO:0019755 55/18614 4.217124e-05 0.02261553 0.02014524
## GO:0001503 429/18614 4.585778e-05 0.02261553 0.02014524
## GO:0015669 22/18614 1.114462e-04 0.04710989 0.04196410
## GO:0097553 201/18614 1.287023e-04 0.04760376 0.04240401
## GO:0061564 479/18614 1.652384e-04 0.05432672 0.04839263
## GO:0031214 173/18614 2.003034e-04 0.05926979 0.05279577
## geneID
## GO:0030198 COL23A1/COL9A3/SMOC2/CCN2/BMP2/MATN2/DPT/CSGALNACT1/ABI3BP/TNFRSF11B/WDR72/KLK4/COL4A6/VIT
## GO:0043062 COL23A1/COL9A3/SMOC2/CCN2/BMP2/MATN2/DPT/CSGALNACT1/ABI3BP/TNFRSF11B/WDR72/KLK4/COL4A6/VIT
## GO:0045229 COL23A1/COL9A3/SMOC2/CCN2/BMP2/MATN2/DPT/CSGALNACT1/ABI3BP/TNFRSF11B/WDR72/KLK4/COL4A6/VIT
## GO:0015671 IPCEF1/HBA2/HBA1/HBB
## GO:0019755 SLC4A4/SLC26A4/SLC26A7/HBA2/HBA1/HBB
## GO:0001503 LTF/MYOC/WNT11/CHRDL1/ZBTB16/CCN2/EGR2/BMP2/GPC3/CSGALNACT1/RANBP3L/ADGRV1/ROR2/BMP8A/RORB/GDF10
## GO:0015669 IPCEF1/HBA2/HBA1/HBB
## GO:0097553 P2RX5/PLCH1/CCN2/CCL21/ANK2/ITPR1/GRIN2C/HAP1/CD19/RYR2
## GO:0061564 EPHA3/PAK3/RPS6KA5/KCNA1/SRF/EGR2/EPHA5/LRRC4C/NCAM1/CNTN5/PLEKHG4B/SEMA3D/EPHB1/SLITRK5/RELN/APOD
## GO:0031214 LTF/WNT11/ODAM/BMP2/GPC3/ADGRV1/WDR72/KLK4/ROR2
## Count
## GO:0030198 14
## GO:0043062 14
## GO:0045229 14
## GO:0015671 4
## GO:0019755 6
## GO:0001503 16
## GO:0015669 4
## GO:0097553 10
## GO:0061564 16
## GO:0031214 9# molecular function GO
ego_MF_DW <- enrichGO(gene = DWDegs$external_gene_name.x, OrgDb = org.Hs.eg.db, keyType = 'SYMBOL', ont = 'MF',pAdjustMethod = 'BH',pvalueCutoff = 0.1, qvalueCutoff = 0.1)
View(ego_MF_DW)
barplot(ego_MF_DW,showCategory = 10)
dotplot(ego_MF_DW, showCategory= 10 )
heatplot(ego_MF_DW, showCategory = 2)
head(ego_MF_DW,10)## ID Description
## GO:0005201 GO:0005201 extracellular matrix structural constituent
## GO:0005344 GO:0005344 oxygen carrier activity
## GO:0004714 GO:0004714 transmembrane receptor protein tyrosine kinase activity
## GO:0031720 GO:0031720 haptoglobin binding
## GO:0004713 GO:0004713 protein tyrosine kinase activity
## GO:0043177 GO:0043177 organic acid binding
## GO:0022803 GO:0022803 passive transmembrane transporter activity
## GO:0019199 GO:0019199 transmembrane receptor protein kinase activity
## GO:0004601 GO:0004601 peroxidase activity
## GO:0005539 GO:0005539 glycosaminoglycan binding
## GeneRatio BgRatio pvalue p.adjust qvalue
## GO:0005201 11/228 167/18369 7.827874e-06 0.003561683 0.003188829
## GO:0005344 4/228 14/18369 2.098801e-05 0.004774771 0.004274925
## GO:0004714 6/228 60/18369 9.795986e-05 0.014857245 0.013301917
## GO:0031720 3/228 10/18369 2.123498e-04 0.021775459 0.019495900
## GO:0004713 8/228 137/18369 3.156591e-04 0.021775459 0.019495900
## GO:0043177 10/228 212/18369 3.277564e-04 0.021775459 0.019495900
## GO:0022803 16/228 479/18369 3.350071e-04 0.021775459 0.019495900
## GO:0019199 6/228 77/18369 3.894296e-04 0.022148807 0.019830164
## GO:0004601 5/228 55/18369 5.919256e-04 0.028884076 0.025860353
## GO:0005539 10/228 232/18369 6.640351e-04 0.028884076 0.025860353
## geneID
## GO:0005201 COL23A1/COL9A3/EFEMP1/MATN2/MMRN1/FRAS1/DPT/ABI3BP/SBSPON/PODN/COL4A6
## GO:0005344 IPCEF1/HBA2/HBA1/HBB
## GO:0004714 EPHA3/EFEMP1/EPHA5/EPHB1/KIT/ROR2
## GO:0031720 HBA2/HBA1/HBB
## GO:0004713 EPHA3/RPS6KA5/EFEMP1/BLK/EPHA5/EPHB1/KIT/ROR2
## GO:0043177 AKR1C2/CRABP1/FABP4/GLDC/AKR1C1/SERPINA5/HBA2/RYR2/HBA1/HBB
## GO:0022803 KCNK2/TRPM3/P2RX5/CHRNA4/OCA2/KCNA1/KCNJ13/GJB6/SLC26A7/GPM6A/ITPR1/GRIN2C/CLCNKB/KCNIP4/RYR2/SLC5A8
## GO:0019199 EPHA3/EFEMP1/EPHA5/EPHB1/KIT/ROR2
## GO:0004601 IPCEF1/TPO/HBA2/HBA1/HBB
## GO:0005539 LTF/COL23A1/SMOC2/CCN2/LYVE1/RSPO3/PCOLCE2/SERPINA5/LAYN/VIT
## Count
## GO:0005201 11
## GO:0005344 4
## GO:0004714 6
## GO:0031720 3
## GO:0004713 8
## GO:0043177 10
## GO:0022803 16
## GO:0019199 6
## GO:0004601 5
## GO:0005539 10# KEGG enrichment
eWP_DW<-enrichWP(gene=DWDegs$entrezgene_id, organism ='Homo sapiens',pvalueCutoff = 0.5, qvalueCutoff = 0.5)
head(eWP_DW, 10)## ID
## WP4875 WP4875
## WP2840 WP2840
## WP5053 WP5053
## WP236 WP236
## WP4538 WP4538
## WP5231 WP5231
## WP2406 WP2406
## WP15 WP15
## WP558 WP558
## WP4746 WP4746
## Description
## WP4875 Disruption of postsynaptic signaling by CNV
## WP2840 Hair follicle development: cytodifferentiation - part 3 of 3
## WP5053 Development of ureteric collection system
## WP236 Adipogenesis
## WP4538 Regulatory circuits of the STAT3 signaling pathway
## WP5231 Hippocampal synaptogenesis and neurogenesis
## WP2406 Cardiac progenitor differentiation
## WP15 Selenium micronutrient network
## WP558 Complement and coagulation cascades
## WP4746 Thyroid hormones production and peripheral downstream signaling effects
## GeneRatio BgRatio pvalue p.adjust qvalue
## WP4875 4/122 33/8442 0.001228598 0.1473777 0.1403878
## WP2840 6/122 87/8442 0.001566683 0.1473777 0.1403878
## WP5053 5/122 60/8442 0.001681114 0.1473777 0.1403878
## WP236 7/122 131/8442 0.002818685 0.1853286 0.1765387
## WP4538 5/122 78/8442 0.005290059 0.2619259 0.2495031
## WP5231 3/122 27/8442 0.006687458 0.2619259 0.2495031
## WP2406 4/122 53/8442 0.007044874 0.2619259 0.2495031
## WP15 5/122 86/8442 0.007967327 0.2619259 0.2495031
## WP558 4/122 58/8442 0.009660250 0.2822940 0.2689052
## WP4746 5/122 94/8442 0.011467383 0.2950422 0.2810488
## geneID Count
## WP4875 1740/2905/23237/6262 4
## WP2840 7704/1490/10804/1959/2354/9622 6
## WP5053 7481/650/80144/2719/2674 5
## WP236 10891/3977/1959/650/8471/1675/2662 7
## WP4538 53832/2690/3977/5596/64798 5
## WP5231 9252/10891/4684 3
## WP2406 3170/4684/3815/4920 4
## WP15 1491/348303/3039/1733/3043 5
## WP558 1380/2159/5104/1675 4
## WP4746 5172/10891/7173/9882/1733 5- Use the pathview R package to visualize one pathway you find enriched using the up-regulated gene list.
We can use the library pathway and specifically the function pathway that generates a local cartoon of the pathway of interest underlying the DEGs genes using specific colors. A measure of the DE is needed, here we use the Log Fold Change. The pathway we represent is chosen by looking at the top 10 expressed pathways from point 4, specifically, we selected the ‘Vitamin D receptor pathway’ for which we found in https://www.genome.jp/entry/hsa:7421 the corresponding pathway ID code hsa04928 for Parathyroid hormone synthesis, secretion, and action.
library(pathview)
logFC <-UPDegs$logFC
names(logFC) <-UPDegs$entrezgene_id
pathview(gene.data = logFC, pathway.id = 'hsa04928',species = 'human') ## 'select()' returned 1:1 mapping between keys and columns## Info: Working in directory /home/andrea/Desktop/magistrale_Qcb/2master_QCB_second_semester_first_year/Bioinformatics_resource/Bioinformatics_Romanel_project-## Info: Writing image file hsa04928.pathview.png- Identify which transcription factors (TFs) have enriched scores in the promoters of all up-regulated (or down-regulated if you prefer) genes.
- use a window of 500 nucleotides upstream each gene.
We choose to search for transcription factors (TFs) with enriched scores in the promoters of all up-regulated genes. We first retrieved the sequences of the promoters using the function getSequence and explicating the promoter identifier in the parameter seqType, using a window of 500 nucleotides upstream. Then, we used the DNAStringSet function to create a DNAString object, needed to operate the motif enrichment. We can now operate the enrichment and thanks to the function groupReport we obtain the TFenrichments across many sequences. By looking at the top 5 ranked transcription factors we can see that all the p-values are highly significative, meaning that the motifs are over-represented in the test sequences suggesting that the motifs are present for a reason, such as a biological function. There is also a similarity on the Weighted matrices with a high presence of G bases, indeed there are positions that are conserved in all 5 motifs.
Seq_up <- getSequence(id = UPDegs$external_gene_name.x, type = 'hgnc_symbol',seqType = 'gene_flank', upstream = 500,mart = ensembl)
# gene_flank rapresnet the promoters of the genes
View(Seq_up)
library(MotifDb) # an annotateed collection of motifs## Loading required package: GenomicRanges## Loading required package: GenomeInfoDb## Loading required package: Biostrings## Loading required package: XVector##
## Attaching package: 'XVector'## The following object is masked from 'package:purrr':
##
## compact##
## Attaching package: 'Biostrings'## The following object is masked from 'package:base':
##
## strsplit## See system.file("LICENSE", package="MotifDb") for use restrictions.new_Seq <- DNAStringSet(Seq_up$gene_flank)
library(PWMEnrich) # for pattern matching
library(PWMEnrich.Hsapiens.background) # package containing background models
library(seqLogo) # plotting the seqLogo of the corresponding motifs## Loading required package: grid##
## Attaching package: 'grid'## The following object is masked from 'package:Biostrings':
##
## patterndata("PWMLogn.hg19.MotifDb.Hsap")
res_initial = motifEnrichment(new_Seq,PWMLogn.hg19.MotifDb.Hsap,score = 'affinity')## Scanning sequence 1 / 688## Scanning sequence 2 / 688## Scanning sequence 3 / 688## Scanning sequence 4 / 688## Scanning sequence 5 / 688## Scanning sequence 6 / 688## Scanning sequence 7 / 688## Scanning sequence 8 / 688## Scanning sequence 9 / 688## Scanning sequence 10 / 688## Scanning sequence 11 / 688## Scanning sequence 12 / 688## Scanning sequence 13 / 688## Scanning sequence 14 / 688## Scanning sequence 15 / 688## Scanning sequence 16 / 688## Scanning sequence 17 / 688## Scanning sequence 18 / 688## Scanning sequence 19 / 688## Scanning sequence 20 / 688## Scanning sequence 21 / 688## Scanning sequence 22 / 688## Scanning sequence 23 / 688## Scanning sequence 24 / 688## Scanning sequence 25 / 688## Scanning sequence 26 / 688## Scanning sequence 27 / 688## Scanning sequence 28 / 688## Scanning sequence 29 / 688## Scanning sequence 30 / 688## Scanning sequence 31 / 688## Scanning sequence 32 / 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688## Scanning sequence 634 / 688## Scanning sequence 635 / 688## Scanning sequence 636 / 688## Scanning sequence 637 / 688## Scanning sequence 638 / 688## Scanning sequence 639 / 688## Scanning sequence 640 / 688## Scanning sequence 641 / 688## Scanning sequence 642 / 688## Scanning sequence 643 / 688## Scanning sequence 644 / 688## Scanning sequence 645 / 688## Scanning sequence 646 / 688## Scanning sequence 647 / 688## Scanning sequence 648 / 688## Scanning sequence 649 / 688## Scanning sequence 650 / 688## Scanning sequence 651 / 688## Scanning sequence 652 / 688## Scanning sequence 653 / 688## Scanning sequence 654 / 688## Scanning sequence 655 / 688## Scanning sequence 656 / 688## Scanning sequence 657 / 688## Scanning sequence 658 / 688## Scanning sequence 659 / 688## Scanning sequence 660 / 688## Scanning sequence 661 / 688## Scanning sequence 662 / 688## Scanning sequence 663 / 688## Scanning sequence 664 / 688## Scanning sequence 665 / 688## Scanning sequence 666 / 688## Scanning sequence 667 / 688## Scanning sequence 668 / 688## Scanning sequence 669 / 688## Scanning sequence 670 / 688## Scanning sequence 671 / 688## Scanning sequence 672 / 688## Scanning sequence 673 / 688## Scanning sequence 674 / 688## Scanning sequence 675 / 688## Scanning sequence 676 / 688## Scanning sequence 677 / 688## Scanning sequence 678 / 688## Scanning sequence 679 / 688## Scanning sequence 680 / 688## Scanning sequence 681 / 688## Scanning sequence 682 / 688## Scanning sequence 683 / 688## Scanning sequence 684 / 688## Scanning sequence 685 / 688## Scanning sequence 686 / 688## Scanning sequence 687 / 688## Scanning sequence 688 / 688## Calculating motif enrichment scores ...TF_enrichment <- groupReport(res_initial)
TF_enrichment## An object of class 'MotifEnrichmentReport':
## rank target id raw.score p.value
## 1 1 PDLIM5 PDLIM5 4.03024318240181 6.93435108583786e-75
## 2 2 GPD1 GPD1 6.28306364825295 9.89287412384532e-74
## 3 3 JUN M4591_1.02 3.07552583607849 1.46079776308636e-73
## 4 4 JUNB M4623_1.02 3.09492858866266 1.1446527158344e-72
## 5 5 CEBPB M4556_1.02 2.5085205500081 1.65649558189844e-70
## 6 6 MAFK M4573_1.02 2.6564802703635 1.95372765047626e-70
## 7 7 ZDHHC5 ZDHHC5 2.56104904937184 4.51981324514863e-70
## 8 8 AHR M2917_1.02 3.6904461952882 6.41348098968778e-70
## 9 9 SREBF1 M2387_1.02 1.50928254592526 1.8905870707718e-69
## 10 10 MAFF M4572_1.02 2.52431418094361 2.13168795000711e-69
## ... ... ... ... ... ...
## 2287 2019.5 Oct-1 NBT06/Oct-1.pwm 1.43240607704683 1
## top.motif.prop
## 1 0.210755813953488
## 2 0.206395348837209
## 3 0.206395348837209
## 4 0.196220930232558
## 5 0.22093023255814
## 6 0.213662790697674
## 7 0.199127906976744
## 8 0.209302325581395
## 9 0.17296511627907
## 10 0.204941860465116
## ... ...
## 2287 0.0247093023255814plot(TF_enrichment[1:5]) # the first 5
PDLIM5,JUN,JUNB,CEBPB based on the literature are all genes related to
thyroid carcinoma, more in general all the 5 genes are assosciated to
carcinoma.
- Select one among the top enriched TFs, compute the empirical distributions of scores for all PWMs that you find in MotifDB for the selected TF and determine for all of them the distribution (log2) threshold cutoff at 99.75%.
We chose to analyze the first TF among the top enriched ones. We create an object containing the metadata related to the chosen TF and the corresponding PPM using the function query(). We use the PPM to compute the PWM matrix and the distribution of scores, using the function motifEcdf. Finally, we determined the threshold cutoff at 99.75% applying to the ecdf the quantile function and explicating the corresponding quantile wanted (here 1-25e-4).
tfs<-TF_enrichment$target[1]
tfmotif <- query(MotifDb,tfs)
PWM_tfs <- toPWM(as.list(tfmotif))
ecdf_tfs <- motifEcdf(PWM_tfs, organism = 'hg19', quick = T)## Starting scanning with motif Hsapiens-hPDI-PDLIM5## Scanning all sequences with motif 1 / 1threshlod = log2(quantile(ecdf_tfs$`Hsapiens-hPDI-PDLIM5`, 1-25e-4))
threshlod## 99.75%
## 9.048089In this case the threshold cutoff at 99.75% is 9.048089.
- Identify which up-regulated (or down-regulated depending on the choice you made at point 7) genes have a region in their promoter (defined as previously) with binding scores above the computed thresholds for any of the previously selected PWMs.
- Use pattern matching as done during the course
We first compute the scores using the motifScores function. For each PWM we apply the cutoff and we obtain matrices in which the rows represent the promoters and the columns the motifs. Here we obtain a matrix 688 x 1 since we are investigating one motif (=the one of the TF selected in the point above). Each matrix box is associated with a score representing the match of the TF motif to the promoter. We then calculate how good is the TF in binding the promoters, by calculating the frequency of the matches that are above a fixed threshold (the threshold computed in the previous point at 99.75%).
score = motifScores(new_Seq, PWM_tfs, raw.scores = F, cutoff = threshlod)## Scanning sequence 1 / 688## Scanning sequence 2 / 688## Scanning sequence 3 / 688## Scanning sequence 4 / 688## Scanning sequence 5 / 688## Scanning sequence 6 / 688## Scanning sequence 7 / 688## Scanning sequence 8 / 688## Scanning sequence 9 / 688## Scanning sequence 10 / 688## Scanning sequence 11 / 688## Scanning sequence 12 / 688## Scanning sequence 13 / 688## Scanning sequence 14 / 688## Scanning sequence 15 / 688## Scanning sequence 16 / 688## Scanning sequence 17 / 688## Scanning sequence 18 / 688## Scanning sequence 19 / 688## Scanning sequence 20 / 688## Scanning sequence 21 / 688## Scanning sequence 22 / 688## Scanning sequence 23 / 688## Scanning sequence 24 / 688## Scanning sequence 25 / 688## Scanning sequence 26 / 688## Scanning sequence 27 / 688## Scanning sequence 28 / 688## Scanning sequence 29 / 688## Scanning sequence 30 / 688## Scanning sequence 31 / 688## Scanning sequence 32 / 688## 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688## Scanning sequence 634 / 688## Scanning sequence 635 / 688## Scanning sequence 636 / 688## Scanning sequence 637 / 688## Scanning sequence 638 / 688## Scanning sequence 639 / 688## Scanning sequence 640 / 688## Scanning sequence 641 / 688## Scanning sequence 642 / 688## Scanning sequence 643 / 688## Scanning sequence 644 / 688## Scanning sequence 645 / 688## Scanning sequence 646 / 688## Scanning sequence 647 / 688## Scanning sequence 648 / 688## Scanning sequence 649 / 688## Scanning sequence 650 / 688## Scanning sequence 651 / 688## Scanning sequence 652 / 688## Scanning sequence 653 / 688## Scanning sequence 654 / 688## Scanning sequence 655 / 688## Scanning sequence 656 / 688## Scanning sequence 657 / 688## Scanning sequence 658 / 688## Scanning sequence 659 / 688## Scanning sequence 660 / 688## Scanning sequence 661 / 688## Scanning sequence 662 / 688## Scanning sequence 663 / 688## Scanning sequence 664 / 688## Scanning sequence 665 / 688## Scanning sequence 666 / 688## Scanning sequence 667 / 688## Scanning sequence 668 / 688## Scanning sequence 669 / 688## Scanning sequence 670 / 688## Scanning sequence 671 / 688## Scanning sequence 672 / 688## Scanning sequence 673 / 688## Scanning sequence 674 / 688## Scanning sequence 675 / 688## Scanning sequence 676 / 688## Scanning sequence 677 / 688## Scanning sequence 678 / 688## Scanning sequence 679 / 688## Scanning sequence 680 / 688## Scanning sequence 681 / 688## Scanning sequence 682 / 688## Scanning sequence 683 / 688## Scanning sequence 684 / 688## Scanning sequence 685 / 688## Scanning sequence 686 / 688## Scanning sequence 687 / 688## Scanning sequence 688 / 688length(which(apply(score,1,sum) > 0)) /length(score) ## [1] 0.9287791# we count the boxes that have alt least one score major that the threshold, then divided int for their number to get the frequency 0.9287791 is the result, indicating that the selected TF can bind almost all the promoters with a good affinity, as we expected since we chose the top enriched TF.
- Use STRING database to find PPI interactions among differentially expressed genes and export the network in TSV format.
We created a .txt file containing all the gene_names of the up-regulated genes identified in the previous points. We then uploaded the file to the STRING tool to obtain and create a network. We finally downloaded the output in .tsv format, the file contains all the information on the edges found between the input genes (=our nodes).
write.table(unique(UPDegs$external_gene_name.x),sep = '\t', file = 'UPDEGs.txt',row.names = F, col.names = F, quote = T)- Import the network in R and using igraph package and identify and plot the largest connected component.
We first created the node annotations using bioMart. We filtered using the up-regulated genes names and selected as attributes the gene id, the description, the biotype, the start and end position, the chromosome name, and the strand. We then reduced the nodes, eliminating repetitions. We imported the information obtained by STRING and filtered it keeping only the edges between nodes that were present in our node variable. We operated a similar operation eliminating the nodes not found in the link variable. We finally constructed the network using the library igraph, specifically the function graph_from_data_frame, defining as edges the link variable and as vertices the nodes variable. The identification of the largest connected component was possible using the function components() of igraph with which it is possible to specify if we are looking at weakly or strongly connected components. It is therefore possible to obtain the cluster of membership for each node and identify the largest cluster between the defined ones. Here we found cluster 1 as the largest, with a total of 510 nodes, it was possible to extract it from the total graph using the function induced_subgraph, specifying the nodes to extract. Since the total graph and the subgraph are large graphs with >500 nodes we decided to plot them using the ggnet2 function, part of the GGally package. We also decided to resize and color the nodes based on the additional information on the number of outdegrees, meaning the number of edges that are coming out from each node. From both plots, we can see that the more central node are the ones characterized by the higher outdegree, as expected based on their position. Contrary to the first graph, in the second one all the nodes are connected, indeed we selected only the strongly connected components.
library(igraph)##
## Attaching package: 'igraph'## The following object is masked from 'package:Biostrings':
##
## union## The following object is masked from 'package:XVector':
##
## path## The following object is masked from 'package:GenomicRanges':
##
## union## The following object is masked from 'package:IRanges':
##
## union## The following object is masked from 'package:S4Vectors':
##
## union## The following objects are masked from 'package:BiocGenerics':
##
## normalize, path, union## The following object is masked from 'package:clusterProfiler':
##
## simplify## The following objects are masked from 'package:lubridate':
##
## %--%, union## The following objects are masked from 'package:dplyr':
##
## as_data_frame, groups, union## The following objects are masked from 'package:purrr':
##
## compose, simplify## The following object is masked from 'package:tidyr':
##
## crossing## The following object is masked from 'package:tibble':
##
## as_data_frame## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
##
## unionlibrary(GGally)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2# we use the general deg
nodes <- getBM(attributes = c('external_gene_name','ensembl_gene_id','description','gene_biotype','start_position','end_position','chromosome_name',
'strand'), filters = c('ensembl_gene_id'), values =UPDegs$ensembl_gene_id, mart = ensembl)
nodes <- unique(nodes[ ,c(1,3:6)]) # colonna 1 piu da 3 a 6
link <- read.delim('string_interactions.tsv') # update the string file
link <- link %>% filter(!link$X.node1 %in% c(setdiff(c(link$node2,link$X.node1), nodes$external_gene_name)) & !link$node2 %in% c(setdiff(c(link$node2,link$X.node1), nodes$external_gene_name)))
nodes <- nodes %>% filter(!nodes$external_gene_name %in% c(setdiff( nodes$external_gene_name,c(link$node2,link$X.node1))))
net <- graph_from_data_frame(d = link , directed = F, vertices = nodes )
net## IGRAPH 919750c UN-- 522 3682 --
## + attr: name (v/c), description (v/c), gene_biotype (v/c),
## | start_position (v/n), end_position (v/n), node1_string_id (e/c),
## | node2_string_id (e/c), neighborhood_on_chromosome (e/n), gene_fusion
## | (e/n), phylogenetic_cooccurrence (e/n), homology (e/n), coexpression
## | (e/n), experimentally_determined_interaction (e/n),
## | database_annotated (e/n), automated_textmining (e/n), combined_score
## | (e/n)
## + edges from 919750c (vertex names):
## [1] SLCO4A1--ABCC3 RXRG --ABTB2 ABTB2 --SPTBN2 FSTL3 --ADAM12
## [5] TIMP1 --ADAM12 COL1A1 --ADAM12 MMP7 --ADAM12 ADAM12 --COL1A2
## + ... omitted several edgesnet<-igraph::simplify(net)
deg_net <- igraph::degree(net, mode ='out')
clrvek_net <- pmax(0,pmin(deg_net,4))
clrname_net <- c('0'='lightgray','1'='#FF1744','2'='#F0A830','3'='#A155B9','4'='#165BAA')
ggnet2(net,size = 'outdegree', max_size = 3 ,color = clrvek_net, label = T, label.size = 2, legend.position = 'bottom', size.cut = 3, color.legend = 'clr~degree',palette = clrname_net)
c <- igraph::components(net, mode ='strong')
c## $membership
## SEMA3F AOC1 PLXND1 ITGA3 CRLF1 TNFRSF12A ALDH3B1
## 1 1 1 1 1 1 1
## GGCT TENM1 MLXIPL MRC2 PLAUR ALOX5 MAMLD1
## 1 1 1 1 1 1 1
## BID MARCO CYP24A1 NRXN3 DEPDC1B DAPK2 VCAN
## 1 1 1 1 1 1 1
## RAB27B TNC LTBP1 NFE2L3 ENTPD2 CBLN4 PKP2
## 1 1 1 1 1 1 1
## PRDM1 RASGRF1 RIMBP2 COL11A1 BCAT1 LZTS1 CDH3
## 1 1 1 1 1 1 1
## NGFR ERBB3 NGEF CACNB1 SYT1 RAB27A FSTL3
## 1 1 1 1 1 1 1
## TRPC5 P4HA2 TP63 IGF2BP2 CA12 CACNG4 FAP
## 1 1 1 1 1 1 1
## BRINP1 PGR FOLH1 CEACAM6 FAT2 NOX4 ADAMTS2
## 1 1 1 1 1 1 1
## SULT2B1 FXYD5 ICAM1 NRCAM TGM1 TYRO3 TBX15
## 1 1 1 1 1 1 1
## PALM MMP11 LGALS1 KDELR3 RASD2 SLCO4A1 BIRC7
## 1 1 1 1 1 1 1
## EEF1A2 TRIB3 E2F1 CST4 CELF4 PCSK1N PHEX
## 1 1 1 1 1 1 1
## TIMP1 PIN4 TSC22D1 CBLN1 CCL22 CCL17 CTSH
## 1 1 1 1 1 1 1
## RHOV MYEF2 TUSC3 KCNN4 CLIP3 BBC3 ICAM4
## 1 2 1 1 1 1 1
## ICAM5 SIGLEC6 PDE4C ISYNA1 COMP MAG TMEM59L
## 1 1 1 1 1 1 1
## HPN SCN1B MET NOD1 AGR2 CORO2A TGFBR1
## 1 1 1 1 1 3 1
## NPDC1 UNC5B SPOCK2 WNT3 COL1A1 ABCC3 FAM20A
## 1 1 1 1 1 1 1
## NMU GALNT7 GLRB CTSC B3GAT1 DTX4 CCND1
## 1 1 1 1 1 1 1
## MDK ADTRP TPD52L1 PERP MDFI THBS4 CDH6
## 1 1 1 1 1 1 1
## SLC27A6 FGF1 CLDN16 COL7A1 SLC30A3 FNDC4 EVA1A
## 1 1 1 1 1 1 1
## FN1 IGFBP2 GNLY PASK CD207 QSOX1 HPCAL4
## 1 1 1 1 1 1 1
## ST6GALNAC5 MFAP2 ECE1 GALE NRP2 B4GALT6 NPC2
## 1 1 1 1 1 1 1
## IQSEC3 TGFBI DUSP4 CLU SFTPA1 PLAU COL10A1
## 1 1 1 1 1 1 1
## G0S2 KCNJ2 SLPI SDC4 PSG8 GRM4 TREM1
## 1 1 1 1 1 1 1
## SOX4 RUNX2 MYRF TMEM255A C3 PCSK2 LAMP5
## 1 1 1 1 1 1 1
## CITED1 MGAT3 KRT17 KLK10 APOE PXDN GDF15
## 1 1 1 1 1 1 1
## COL5A1 HIGD1B NAPSA PDLIM4 PPP1R1B LGALS3 RAMP1
## 1 1 1 1 1 1 1
## PTPRE ITGB4 TRIM47 DLG4 VSTM2L CHI3L1 POSTN
## 1 1 4 1 1 1 1
## KL LOXL2 LRP4 DSC3 APLNR C5AR2 CLDN10
## 1 1 1 1 1 1 1
## HRK NT5E GLS2 RDH5 HEY2 WNT10A PLXNC1
## 1 1 1 1 1 1 1
## SCEL RAPGEF5 MYO1G ADAMTS7 GALNT5 GABBR2 FGFBP1
## 1 1 3 1 1 1 1
## TMPRSS4 MMP7 TRIM29 POU2F3 ITGA11 STRA6 CYP1B1
## 1 1 1 1 1 1 1
## TRIM54 LOXL4 DUSP5 ENTPD1 ADAMTS14 HCN4 PDE5A
## 4 1 1 1 1 1 1
## B4GALNT3 ERP27 PIANP AMIGO2 DUSP6 NTRK3 MFGE8
## 1 1 1 1 1 1 1
## CDH11 CDH13 TMC6 PMAIP1 GRB7 FCN3 SYTL1
## 1 1 1 1 1 1 1
## CYP4B1 RXRG CRABP2 XPR1 ECM1 SUSD4 ETNK2
## 1 1 1 1 1 1 5
## HES6 STAC IL17RD TMEM108 HAPLN1 CXCL14 GABRB2
## 1 1 1 1 1 1 1
## TNFRSF21 IGFBP3 SYTL5 IGSF1 ARHGAP36 CDKN2B CDKN2A
## 1 1 1 1 1 1 1
## SHC3 LCN2 ADAM12 ADM MPZL2 CDH22 HMGA2
## 1 1 1 1 1 1 1
## MKX PRSS23 THRSP ADAMTS12 EPS8 GLT1D1 TMEM132D
## 1 1 1 1 1 6 6
## TMEM163 CYSLTR2 SPOCK1 ENPP3 PDE1C ADCY8 MMP16
## 1 1 1 1 1 1 1
## TIAM1 GDF6 CD109 B3GNT7 SLC34A2 GRHL3 XDH
## 1 1 1 1 1 1 1
## SHROOM4 CD1A CDA LAD1 TNNI1 RUNX1 PDE9A
## 1 1 1 1 1 1 1
## S100B S100A1 LY6E SCGB3A1 PLXDC1 PLCD3 AQP5
## 1 1 1 1 1 1 1
## PDZK1IP1 DHRS3 ALPL MXRA8 DIRAS3 KLHDC8A SPATA18
## 1 1 1 1 1 1 1
## INHBB BNIPL CDC42EP3 S100A11 IVL S100A9 TGFA
## 1 1 1 1 1 1 1
## CLDN1 ELF3 ADORA1 ADAMTS9 GRIK3 LIPH MST1R
## 1 1 1 1 1 1 1
## PRSS12 ITGA2 EDIL3 ESM1 FOXQ1 TMEM200A COL1A2
## 1 1 1 1 1 1 1
## CTHRC1 DCSTAMP CLDN2 RET PDZRN4 ABTB2 SPINT1
## 1 1 1 1 1 1 1
## PLD4 TMEM130 BEAN1 ANPEP SCG5 PIP5KL1 CDT1
## 1 1 1 1 1 1 1
## CYP2S1 CDC42EP5 SEMA6B GGT6 KLK6 KLK11 KRT80
## 1 1 1 1 1 1 1
## ANGPTL4 IGFBP6 BMP1 PHYHIP COL3A1 SFTPB KLK7
## 1 1 1 1 1 1 1
## ATF5 CPT1C STK32A CXCL8 TM4SF4 TM4SF1 ADRA1B
## 1 1 1 1 1 1 1
## CST2 CST1 SEMA3E LONRF2 CDH2 KCNS3 HS6ST2
## 1 1 1 1 1 1 1
## LRG1 KRT19 KRT15 APLN ENC1 P2RY6 COL8A2
## 1 1 1 1 1 1 1
## FRMD5 GAP43 FRMD3 TPSAB1 MACROD2 ZMAT3 SYT12
## 1 1 1 1 1 1 1
## SAA1 TNFRSF10C CSPG4 PC C11orf80 SPTBN2 PHLDA3
## 1 1 1 1 6 1 1
## ZCCHC12 GOLT1A RIN1 SEZ6L2 CHST2 CST6 LPL
## 1 5 1 1 1 1 1
## SFN ETV4 CALCB DOK7 RIMS2 FUT2 B3GNT8
## 1 1 1 1 1 1 1
## CTXN1 CDH4 FJX1 SLITRK4 ALOX15B FCER1A B3GNT3
## 1 1 1 1 1 1 1
## F2R PENK CCL13 TNFSF15 PHLDA2 PLAG1 GABRG3
## 1 1 1 1 1 1 1
## EPHB3 NTM C1orf116 GJC1 CSMD1 EPHA10 UPP1
## 1 1 1 1 1 1 1
## OPCML MACC1 IQGAP3 KCNQ3 ADRA2C ALDH1A3 TACSTD2
## 1 1 1 1 1 1 1
## PTP4A3 PROS1 LPAR5 NELL2 JAG2 MANEAL SFTPA2
## 1 1 1 1 1 1 1
## ROR1 MUC1 AHNAK2 NAT8L THBS2 GLDN ESPN
## 1 1 1 1 1 1 1
## TMPRSS6 SLIT1 GJA4 PLEKHN1 PERM1 GABRD TPRG1
## 1 1 1 1 1 1 1
## TMEM215 SLC24A5 IER5L ASAH2 CC2D2B SHISA6 ENTPD8
## 1 2 1 1 1 1 1
## LRRK2 GJB3 FAM111B CXCL17 IL1RAP S100A4 GREB1
## 1 1 1 1 1 1 1
## RYR1 CD55 CPNE4 S100A5 HRH1 S100A2 LAMB3
## 1 1 1 1 1 1 1
## PDLIM7 SIGLEC15 CYSRT1 SERPINA1 TPSB2 DCHS2 COL13A1
## 1 1 1 1 1 1 1
## DPP4 S100A6 C2CD4A F5 PNP PAX9 DMD
## 1 1 1 1 1 1 1
## TGM2 INF2 NHSL2 COL5A2 CDSN PSORS1C1 MUC21
## 1 3 1 1 1 1 1
## HLA-G CD177 PRSS1 CFI C2CD4B CLDN9 PLXNA4
## 1 1 1 1 1 1 1
## NPTXR ZNF469 ELFN1 PSG1 HLA-DQB2 C1orf226 C4orf48
## 1 1 1 1 1 1 1
## ETV5 PTCHD4 GPR162 NOX5 HP TUBB3 SLC22A31
## 1 1 1 1 1 1 1
## EPPK1 SPON1 ARHGAP23 SRCIN1
## 1 1 1 1
##
## $csize
## [1] 510 2 3 2 2 3
##
## $no
## [1] 6net.c <- induced_subgraph(net, V(net)[which(c$membership == 1)])
net.c <- igraph::simplify(net.c)
deg <- igraph::degree(net.c, mode ='out')
clrvek <- pmax(0,pmin(deg,4))
clrname <- c('0'='lightgray','1'='#FF1744','2'='#F0A830','3'='#A155B9','4'='#165BAA')
ggnet2(net.c,size = 'outdegree', max_size = 4 ,color = clrvek, label = T, label.size = 2, legend.position = 'bottom', size.cut = 5, color.legend = 'clr~degree',palette = clrname)